Keyword search — also called lexical search, or BM25 after its most common scoring algorithm — retrieves passages by matching the exact words in a query against the words in a document. A passage ranks higher if query terms appear in it frequently and if those terms are rare across the wider corpus (common words like "the" carry little weight).
Why it matters
Keyword search is fast, transparent, and surprisingly hard to beat for certain queries. If a user types a contract clause number, a product SKU, a name, or any other term that is both precise and distinctive, a keyword index finds it reliably — without the approximation that comes from compressing meaning into a vector. The result is also easy to inspect: you can see exactly which words drove the match.
The limitation is symmetry: a keyword index only recognises words it has seen. A query for "ending a subscription" won't match a paragraph headed "cancellation policy" because none of the words overlap. That is the gap semantic search was built to close — it matches meaning, so synonyms, paraphrases, and conceptual links all surface.
In practice, neither method dominates alone, which is why hybrid search combines both: keyword precision for exact terms, semantic recall for everything else. Together they give a retrieval system a better chance of finding the passage that actually answers a question, regardless of the words used to ask it.