After defining our pairwise similarity distributions and the hyperparameter for the weight of the CNNscore, we can utilize the Open-ComBind framework to select poses from gnina's sampled poses. Binders lacking structural information are used as helper ligands to select docked poses for our ligand of interest that interact with the receptor in similar ways to the helper ligands. Utilizing the pairwise similarity distributions shown in Figure along with the gnina docking scores enables selection of near-native poses more often than when only the gnina score is used to select a pose (Figure and ??). Following Paggi et al. , we compute the "Overall" statistic, which is the weighted average of the different protein classes according to the proportion of FDA-approved drugs for that class of proteins. Open-ComBind increases the Overall percentage of ligands with a correct pose by 6.5% and 4.8% for high-affinity and congeneric series helper ligands, respectively. GPCRs, ion channels, and nuclear receptors have the greatest increase in performance over only the gnina docking score, 8.4%, 7.8%, and 8.5%, respectively. However, in contrast to Paggi et al. , we find that many proteins show a reduced performance when utilizing the Open-ComBind framework relative to gnina. Additionally, for most of the proteins, we see large variances in Open-ComBind's performance with different runs of gnina.