DockM8: An All-in-One Open-Source Platform for Consensus Virtual Screening in Drug Design A PREPRINT Figure illustrates the frequency of occurrence of the DockM8 pose selection methods on the DEKOIS, DUD-E, and LIT-PCBA datasets. From these data, we can see that in general, the scoring-based pose selection methods (KORP-PL, RTMScore, ConvexPLR and the bestpose_* methods) outperform the descriptor-based ones (spyRMSD, RMSD and espsim) on both the DEKOIS and DUD-E datasets. Indeed, the descriptor-based methods do not appear in the 0.01% bracket and only appear rarely in the 0.1% bracket. Due to computational limitations, the descriptor-based methods were not tested on the Lit-PCBA dataset. The KORP-PL and bestpose methods appear the most often in the 0.01% for the DEKOIS (39 and 23 counts respectively) dataset, while the bestpose method was identified as being the top performer in the DUD-E dataset (55 counts in the 0.01% threshold). Additionally, the bestpose_PLANTS method is significantly more represented in the top brackets for the Lit-PCBA dataset. Although we observe a clear advantage in using certain pose selection methods on each of the three datasets, the presence, and nature of a relationship between the type of dataset (decoy or experimental) and the preferred pose selection method requires further investigation.