The importance of Open-ComBind docking components to the pose selection is inspected by removing pieces from the full docking pipeline to see their impact on performance. We see from Figures and? that removing the CNNscore from the pose selection process completely destroys Open-ComBind's ability to select correct poses, dropping from 64.6% to 31.5%. Removing individual features from Open-ComBind does not have as great an effect as removing the CNNscore, but can significantly impact Open-ComBind's pose selection. The interaction fingerprint is pivotal to pose selection. Still, we see that only using the H-bonds (57.5%) or salt bridges (58.2%) gets about the same performance as gnina-only pose selection (58.1%). Using only Hydrophobic contacts in Open-ComBind is not significantly different (p=0.24) from using all of the features. Removing the whole interaction fingerprint and only using the maximum common substructure RMSD between ligand poses slightly decreases performance (62.8%, p=0.03) but still shows improvements over gnina pose selection. Removing both the H-bonds and salt bridges from the Open-ComBind pose selection process gets about the same performance as when they are used (65.8%, p=0.05), further demonstrating the inadequacy of H-Bond and salt bridges for pose selection improvements in Open-ComBind. Pose selection performance should be dependent on the number of helper ligands we use during pose selection. When no helper ligands are used during the pose selection process, we see that the percentage of ligands whose pose is predicted correctly drops ('Gnina(G)' in Figure and). However, we see in Figures and? that performance plateaus as more helper ligands are added. The performance with ten helper ligands, (63.55 and 61.77 for high-affinity and congeneric series helper ligands, respectively) is about the same as using all 20 helper ligands (64.57 and 61.67 for high-affinity and congeneric series helper ligands, respectively).