Generating a larger ensemble will monotonically increase the likelihood of retrieving a bioactive conformation, but for efficiency reasons it is desirable to generate smaller ensembles. As shown in Figures and, selecting the lowest energy poses from a larger ensemble does not improve the retrieval of bioactive conformations with the exception of reducing down to a single conformer. This is due to the lack of diversity of subsets chosen using only energy as a criteria. In Figures , S17, and S18 we show the effect of imposing an RMSD cutoff when selecting conformers. In this case, the conformers of the full 250 conformer ensemble are sorted by increasing energy and we greedily add conformers to the selected subset only if their RMSD to every already selected conformer is greater than an RMSD threshold. This approach results in an improved retrieval rate relative to unbiased sampling or lowest energy selection when the RMSD threshold used to select conformers is similar to the RMSD cutoff used to classify a conformer as matching the experimental structure. For example, selecting 25 RDKit conformers for Platinum 2017 using an RMSD of 1.0 results in ensembles that contain a conformer within 1.0 RMSD of the true conformer 72.7% of the time, compared to 67.8% when unbiased sampling is used and 57.6% when the 25 lowest energy conformations are selected. However, if the RMSD criteria for determining what qualifies as a matching conformation deviates significantly from the RMSD threshold used to select the subset (either lower or higher), unbiased sampling can outperform the filtered subsets. Finally, we note that in our analysis the bioactive conformation depends on the structure of a receptor, which is hidden information from the conformer generator, but we observe similar trends in retrieval rates when we evaluate using a subset 46 of the Crystallography Open Database 47 (see Figure ) which contains a broader array of single molecule experimental structures.