When we compare our overall performance to that of Paggi et al. in Figure , we see that our open-source version tends to do just as well at selecting poses. However, when comparing ComBind to Open-ComBind on each individual protein, we see a different story. ComBind tends to either increase or not change the percentage of ligands whose pose is predicted correctly across all of the proteins in the benchmark dataset. Open-ComBind has widely varying performance across different protein targets, with some protein targets seeing a major boost in performance for their docked ligands, like SMO and NR3C2 with a 70% and 46% increase, respectively. Other protein targets show a drastic decrease in the percentage of ligands whose pose is predicted correctly, for instance, MGLUR5 and NR3C1 with a 30% and 15% decrease, respectively. ComBind only has worse pose selection than Glide on one protein target, while Open-ComBind has worse pose selection than gnina on 9 protein targets across varying protein classes. This is all in spite of the fact that gnina and Glide demonstrate similar pose selection performance across a majority of the protein targets. The RMSD to ground truth of poses selected by gnina is higher than the RMSD of the poses selected by Open-ComBind for the exact same ligand (?? and ??), average RMSDs of 3.17 and 2.91, respectively. Across all of the random seeds, there is a reduction in the average RMSD of the ligands when using Open-ComBind. However, Open-ComBind does not always choose a lower or equivalent RMSD pose for each ligand. This could be due to the fact that some of the featurizations do not contribute to better pose selections.