How does Qdrant combine vector similarity search with structured payload filters?

Qdrant accepts a vector query together with a filter composed of conditions over payload fields. The filter can constrain candidates using boolean logic such as must, should, and must_not, so the final results satisfy application-level constraints while being ranked by vector similarity.

For example, a product search can require category=electronics and price less than 100 while still ranking eligible products by semantic similarity. This is much more useful than retrieving arbitrary nearest neighbors and trying to enforce business constraints afterward.

The trade-off is that filtering can change the effective candidate set and influence search cost. Selective filters should be modeled and indexed appropriately, especially at large scale. Qdrant's query planner and filtered-search behavior are version-dependent, so I would benchmark representative filters.

A common mistake is treating payload filtering as an unrelated second API call. Qdrant's retrieval model allows the filter to participate in the vector query, which is important for correctness as well as performance.