Interactions are identified using the built-in heuristics of Pharmit. From this set of interactions, all possible pharmacophores with at least three features are enumerated. As our goal is to evaluate the effect of different conformer ensembles on virtual screening and not elucidating the best single pharmacophore query, we screen all the enumerated queries. We set a tolerance radius of 1.0 Å and no other constraints (e.g. direction) on each feature. Since Pharmit uses the sub-linear time Pharmer 38 algorithm, despite the combinatorial number of queries and many thousands of compounds, this can be done efficiently. We emphasize that this algorithm finds matches between the specified query and rigid conformers and so the quality of the ensemble is essential. To evaluate virtual screening performance, we use the F1 score, the harmonic mean of the precision and recall, and report the best F1 across all queries.