Cryptic-pocket prediction is compared almost entirely by top-n recovery, which conflates two separable abilities: proposing a candidate at the true site, and ranking it highly enough to be seen. We retained per-candidate overlaps for five candidate-generation methods, evaluated in six configurations, on the CryptoBench test fold. Coverage spanned 14.5 points; conversion of coverage into top-five recovery spanned 36.7. Two rankers over an identical candidate set differed by 10.6 recovery points at equal coverage, isolating ranking exactly. Union coverage saturated at 92.2%, reaching 98.6% on sites of at least eight residues, with residual failures concentrated on small sites. Injecting synthetic competitors drawn from each target’s own wrong-answer score distribution, holding the true site, candidate pool and ranker fixed, reduced top-five recovery by 16.8 points on training folds and 17.0 points on the held-out test fold. Pooling detectors consequently gains nothing at a budget of five and 11.8 points at twenty.
Lacuna, an open-source Python tool for discovering cryptic binding pockets, generates a conformational ensemble from any input structure, detects pockets independently in every conformer, clusters the detections into persistent sites across the ensemble, and ranks those sites with a model fitted on within-structure pairs.
Pockets from a protein language model at locations where geometry finds no concavity improves single-structure recovery by 8.5% (95% CI +4.0 to +13.6) on test-fold data, and lets a five-conformer ensemble match a twenty-conformer one at a third of the wall clock.