We then focused on evaluating the impact of the selection of consensus methods, pose selection methods, scoring function selection, and the number of scoring functions used during the consensus procedure on DockM8's performance. For the rest of this discussion, we define a "DockM8 combination" as a unique combination of a pose selection method, a consensus method, and one or more scoring functions. For each DockM8 combination, we computed the average performance across all targets within the given dataset, using the EF 1% metric as a standard measure. Subsequently, we recorded the frequency with which specific pose selection methods, consensus methods, or scoring functions ranked among the top DockM8 methods when sorted by the EF 1% metric. These rankings were established at several performance thresholds: 1%, 0.1%, and 0.01%. For instance, if we had 1000 DockM8 combinations, the top 1% threshold would include the 10 combinations with the highest EF 1% values. This method was chosen as opposed to calculating the average performance of each method across the whole dataset, which would be less indicative of top-level performance and may favor poorer-performing methods.