Pre-Filtering vs Post-Filtering (and Why Qdrant Does Neither)

Metadata filtering in vector search can reduce recall without obvious failures, particularly when broad filters or combinations of broad conditions interact poorly with common pre-filtering and post-filtering approaches. Pre-filtering guarantees exact results among eligible points but can become expensive for broad matches, while post-filtering is efficient for lenient conditions but may return too few valid results under strict filters. Qdrant instead applies filters during HNSW graph traversal, skipping ineligible points before scoring them, and supplements this approach with filterable HNSW edges for shared payload values and ACORN query-time traversal repairs for sparse or poorly connected filtered subsets. Its query planner estimates filter selectivity and routes each request among standard or ACORN-enhanced graph search, direct payload-index lookup, or full scanning, allowing it to balance recall and cost per query. Benchmark results cited in the post show that ACORN restored recall to 100% for problematic broad-value and multi-condition filters that otherwise produced substantial recall losses.

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