To understand the distinct behavior of the two scoring functions regarding BEDROC and EF1%, we investigated their performance on a per-molecule basis. We found a strong correlation between the Gnina scores derived from DiffDock-L and Vina poses (Figure ). However, we observed that many compounds, particularly decoys, received substantially higher scores from the Vina scoring function for Vina poses than for DiffDock-L poses (see Figure for examples and Figure for a complete set of figures). While pinpointing the exact reason for this behavior is challenging, the VS process appears to benefit from independent pose generation and scoring processes (the Vina pose sampling algorithm employs the Vina scoring function to guide the sampling process toward the scoring function minima). Figure . Vina scores assigned to individual molecules for two representative targets included in DUDE-Z: (A) ADA and (B) UROK. ADA is the target where the most significant improvement in EF1% was observed by switching pose sampling from DiffDock-L to Vina. Many compounds that are active on ADA were correctly assigned favorable, i.e., negative Vina scores for the poses generated by Vina (orange data points located in the lower center of plot (A)). In contrast, UROK is the target where the greatest improvement in EF1% was recorded by switching sampling from Vina to DiffDock-L. Many decoys for UROK were incorrectly assigned favorable Vina scores for the poses generated by Vina (blue data points located in the lower center section of plot (B)).