RDKit remains the method of choice for practical applications. We find that the classical RDKit method is generally superior to DMCG at recovering bioactive conformations (Figures and) and providing conformations suitable for docking (Figure ). Given the current rate of progress, it is likely that newer deep generative methods will be able to outperform RDKit, although this may require adapting the metrics these methods are trained for. We speculate that the performance gap between these two methods is due to DMCG being trained to maximize coverage of the GEOM-QM9 and GEOM-Drugs sets. That is, it prefers to sample uniformly from the space of reasonable conformers while RDKit may sample from something closer to a Boltzmann distribution. This speculation is supported by reports that resampling RDKit ensembles using clustering can substantially improve its coverage metric on GEOM-QM9 and GEOM-Drugs. Energy minimization is a valuable post-processing step. Energy minimizing generated conformers generally improves their ability to recapitulate bioactive conformers (Fig- Larger ensembles are not always better. Larger ensembles will always have higher likelihood at sampling a bioactive conformation, but for reasonable RMSD thresholds, the point of diminishing returns is achieved relatively quickly. It is not necessary to generate many hundreds of conformations to achieve nearly perfect recall within 2 Å RMSD (Figures and). Furthermore, in screening tasks generating larger ensembles can decrease performance (Figures and) due to increasing the number of false positives. Although the best observed ensemble size for maximal pharmacophore screening performance varied dramatically (from one to the maximum of 200, Figure ), the diminishing returns in screening performance and increased computational complexity incurred by increasing the ensemble size suggest a reduced conformational ensemble of less than 25 conformers is likely sufficient for most structure-based screening tasks that rely on conformational sampling. Filtering for structural diversity can enhance the performance of a given ensemble size. Unsurprisingly, when constructing a smaller ensemble from a larger ensemble, there is benefit to increasing diversity by filtering conformations by their respective RMSDs.