We then investigated the performance of the various consensus methods available in DockM8. From Figure , we can see that the Zscore_best method outperforms the other methods on both the DUD-E and DEKOIS datasets (counts of 59 and 55 in the top 0.01% bracket respectively). For the Lit-PCBA dataset, the RbR_best method significantly outperformed the rest (only method represented in the top 0.01% threshold with 6 counts). Interestingly, the Lit-PCBA dataset seems to show different trends relative to the decoy-based datasets (DEKOIS and DUD-E), which we also observed in our above analysis of the pose selection methods. This may reflect inherent differences in the nature of the data and chemical space included in these datasets. Overall, we can observe that the avg methods tend to perform more poorly than their respective best counterpart. This points to the fact that using the best pose (regardless of how the best pose is determined) is a superior strategy to aggregating information from multiple poses.