Glossary:Hybrid search
9 min.
What is Hybrid Search?
Hybrid Search refers to a search method that combines traditional keyword search (BM25 algorithm) with semantic vector search (embeddings). The result: Hits that contain the exact search term are found, as well as content that is thematically relevant but uses different formulations.
The strengths of each method balance the weaknesses of the other. Keyword search is precise for exact terms but fails with synonyms, typos, or unknown technical terms. Semantic search understands meaning and context but tends to become vague for highly specific searches (e.g., product numbers or names). Hybrid Search combines both approaches and consistently delivers relevant results, regardless of how a visitor phrases their question.
According to a study by Elastic Labs (BEIR Benchmark, 2023), hybrid search improves the NDCG@10 value by +18% compared to pure BM25 search — with a calibrated linear approach even by +24% (Source: Elastic Labs, 2023). That's no minor difference. It's the difference between a search that frustrates and one that converts.
At branchly, hybrid search is the foundation of the AI Search module. Over 40 million AI-powered sessions have been processed on the platform — each utilizing this combined methodology to deliver relevant content to visitors (Source: branchly, 2026).
How does Hybrid Search work technically?
Hybrid Search combines two distinct retrieval methods and merges their results into a common ranking pipeline.
1. BM25 — Keyword-based Retrieval
BM25 (Best Match 25) is the industry standard for lexical retrieval. It analyzes how often a search term appears in a document (term frequency), weights that against the rarity of the term across all documents (inverse document frequency), and takes document length into account. BM25 reliably delivers good results when users know the exact term present in the document.
Weakness: If someone types "cheap TV" and the document only contains "affordable TV," BM25 finds no match.
2. Vector Search — Semantic Retrieval
Vector search transforms both the search query and all documents in the index into numerical vectors (so-called embeddings). These vectors represent meaning in a multidimensional space. The more similar two meanings are, the closer their vectors are. The search then finds the documents whose vectors are closest to the query vector.
Weakness: For specific identifiers such as model numbers, names, or technical terms without semantic context, vector search can become inaccurate.
3. Fusion and Re-Ranking
The result lists of both methods are merged. Common fusion methods are Reciprocal Rank Fusion (RRF) and weighted linear combination. In a final step, a re-ranking model often re-evaluates the combined hits to optimize the final order.
Weaviate has measured hybrid search in extensive tests with BEIR datasets (September 2025) with an nDCG@10 value of 0.61 to 0.71 across various domains — a consistently strong value that surpasses pure keyword or vector search alone (Source: Weaviate, September 2025).
Hybrid Search vs. Keyword Search vs. Vector Search
Feature | Keyword Search (BM25) | Vector Search (Embedding) | Hybrid Search |
|---|---|---|---|
Functionality Principle | Exact term match, weighted by frequency and rarity | Meaning similarity through numerical vectors | Combination of BM25 and embedding, merged in ranking |
Strength | Precise for exact terms, product numbers, names | Understands synonyms, paraphrases, context | Precise and context-sensitive — uniting both strengths |
Weakness | Blind to synonyms and semantic variations | Inaccurate for highly specific identifiers | Higher system complexity, more infrastructure effort |
Language Variance | Low (exact match required) | High (understands different formulations) | Very high (covers both) |
Typo Tolerance | No (without additional fuzzy logic) | Conditionally (depending on the embedding model) | Yes (captured through semantic component) |
Multilinguality | Only with separate language indexes | Good (multilingual embedding models available) | Very good — branchly natively supports 101 languages |
Indexing Effort | Low | High (embedding calculation required) | Medium to high (both indexes concurrently) |
Relevance Level | Good for clear queries | Good for vague queries | Consistently high, regardless of query quality |
Those who implement hybrid search exchange a bit more infrastructure effort for significantly better search experiences. For websites with real traffic, this is a clear decision.
Why Search Deficiencies are Expensive
Poor search is not just a UX problem. It is a revenue problem.
According to a Google Cloud / Harris Poll study (2023), 81% of consumers leave a website after a disappointing search experience. The global costs due to search abandonments exceed $2 trillion annually (Source: Google Cloud, 2023). This figure sounds abstract until you calculate how many of these abandonments occur on your own website.
At the same time, the Baymard Institute (2024) shows that 41% of e-commerce sites do not fully support basic search query types (Source: Baymard Institute, 2024). This means that almost every second shop search cannot answer queries outside of tight keyword patterns meaningfully.
Hybrid Search closes this gap because it finds visitors even when they do not search in the language of the product catalog.
Hybrid Search in Practice: Typical Use Cases
E-Commerce
A visitor searches for "waterproof hiking shoes for wide feet" — the shop catalog does not know this exact phrase but includes products with the attributes "waterproof", "hiking shoe", and "width H". Pure keyword search yields no hits. Hybrid Search understands the intent and returns relevant results.
According to Algolia and Forrester, 93% of merchants with AI-powered search report revenue increases, and 50% of all merchants directly cite the search function as a revenue-relevant factor (Source: Algolia/Forrester). The AI Search module from branchly uses hybrid search as its core — product catalogs are semantically explored, not just keyword indexed.
Tourism
Travelers often describe their wishes in natural language: "relaxing vacation by the sea for families with small children." No catalog has this sentence as a keyword. A tourism website with hybrid search understands the intent and suggests suitable offers — in English, Dutch, or Polish, without needing translated content. branchly natively supports 101 languages and serves over 11 million users with this combined technology.
B2B and Financial Services
Experts often search with technical terms that do not appear in the document as such. A consultant searches for "account model without checking account commitment" — the product is internally called "Flexible Investment without Base Service". Hybrid Search bridges this terminology gap. For financial services providers, this is particularly relevant because their content is often shaped by compliance language, while customers search in everyday language.
Hybrid Search and the Enterprise Search Market
The global enterprise search market is growing rapidly. Mordor Intelligence predicts growth from $7.47 billion (2026) to $11.66 billion (2031) with an annual growth rate of 9.31% (CAGR) (Source: Mordor Intelligence). The driver: AI-powered search systems that replace traditional solutions.
Hybrid Search is no longer a niche technology. It is the new standard for websites with serious traffic.
The AI Search module from branchly is based on this technology and is available from €499/month (Starter, 1,000 sessions). For companies looking to make better use of their existing traffic, this is a direct lever.
branchly AI Search: Hybrid Search in Practice
The AI Search module from branchly combines hybrid search with an embedded user interface that is deeply integrated into the website, not sitting as a floating widget layer on top. This depth of integration is the crucial difference.
Classic search widgets reach 0.5–1% of website visitors. branchly search interfaces reach 5–10% as a widget — with a firmly embedded representation even 45–50% interaction rate (Source: branchly customer data, 2026). This is no random difference. When visitors realize that the search actually understands their questions, they use it.
Every search query through branchly generates behavioral data: What terms are being searched? What hits are being clicked? Where do visitors drop out? This data flows back into visitor analysis and helps marketing teams improve their content — a feedback loop that no point solution can build.
The article counterpart AI Search describes the overarching category and refers to hybrid search as one of the central technologies behind it. Those who understand how AI-powered search works in general will find in this article the technical deepening of it.
Related Terms
AI Search
RAG (Retrieval-Augmented Generation)
Natural Language Processing (NLP)
AI Chatbot
Agentic RAG
AI Product Consulting (Advisor)
Frequently Asked Questions
What is hybrid search in simple terms?
Hybrid search combines two methods: keyword search, which finds exact terms, and semantic search, which understands meaning and context. Instead of choosing between the two, hybrid search utilizes both simultaneously and merges the results. The result: users find relevant content, whether they know the exact term or phrase their query freely.
What is the difference between keyword search and vector search?
Keyword search (BM25) finds documents that contain the exact words searched. Vector search (semantic search) converts queries and documents into numerical vectors and finds documents that are semantically similar, even when there is no word match. Keyword search is precise for specific terms, while vector search is better for vague or rephrased queries.
Why is hybrid search better than just vector search?
Vector search excels at meaning similarity but can be inaccurate with highly specific queries, such as product numbers, brand names, or industry acronyms. BM25 reliably finds these exact matches. Hybrid search combines both strengths: it is semantically flexible and precise with unique terms. According to Elastic Labs benchmarks (2023), this improves result relevance by +18 to +24% compared to BM25 alone.
What algorithms are used in hybrid search?
The keyword component is typically based on BM25 or related TF-IDF variants. The semantic component uses Dense Passage Retrieval (DPR), bi-encoder embedding models, or combined models like Contriever. The fusion happens via Reciprocal Rank Fusion (RRF) or a weighted linear combination of both scores. In advanced systems, a Cross-Encoder Re-Ranker often supplements the final ranking.
Does hybrid search support multiple languages?
Yes, if the embedding models used are multilingual. Multilingual models like mE5 or multilingual-e5-large map queries and documents in different languages into the same vector space — thus, an English question can also find German answers. The branchly AI Search module utilizes multilingual embeddings and natively supports 101 languages without requiring translated content to be available.
How do I measure if hybrid search provides better results?
The standard metric is NDCG@10 (Normalized Discounted Cumulative Gain at the top 10 results). It evaluates whether relevant hits appear in the top positions. Other metrics include: MRR (Mean Reciprocal Rank, how high the first relevant hit stands), Recall@K (the proportion of relevant documents in the top K results), and for product search, the Click-Through Rate on search results. Weaviate benchmarks (2025) show nDCG@10 values from 0.61 to 0.71 for hybrid search across various datasets.
Is hybrid search more difficult to implement than classic search?
Yes, hybrid search requires two parallel indexes (inverted index for BM25, vector index for embeddings), infrastructure for embedding computation, and a fusion logic. This involves more effort than pure keyword search. Ready-made platforms like branchly completely take over this technical effort: The AI Search module can be integrated into existing websites without developer effort.
What data do I need to implement hybrid search?
You do not need training data or historical search logs. Hybrid search works out-of-the-box with your existing content: website texts, product catalogs, FAQ pages, blog articles. The embedding model is not trained on your data but applies a pre-trained model to your content. The more structured and extensive your content, the better the results.
How does hybrid search assist with personalization?
Hybrid search is the technical foundation — personalization comes through the combination with contextual data. Branchly combines hybrid search with session context: which pages has the visitor viewed so far? What have they searched for? What language do they speak? These contextual factors influence the re-ranking of search results, making the search not only generally relevant but also relevant to this visitor.
When is hybrid search worthwhile for a website?
Hybrid search is worthwhile as soon as a website has more than a few dozen content pages and visitors are actively searching for something. For e-commerce websites, tourism portals, and content-rich B2B sites, it is useful from day one. Branchly AI Search starts at €499/month for 1,000 sessions and scales with traffic. More decisive than the absolute page count is the question of whether you can afford to lose 81% of visitors after a disappointing search.
