We also evaluated the performance of DockM8 on a subset of the DUD-E benchmarking dataset (28 targets, Figure ). As above, a variety of other methods collected from the literature were included in the comparison, including knowledge-based, empirical, and machine-learning scoring functions (MLSFs), as well as a variety of consensus methodologies (for all associated data, see the Supporting Information). Since the RFScoreVS was partially trained on the DUD-E dataset , we also report the performance of DockM8-max-noRF and As with the DEKOIS dataset, DockM8-max outperforms nearly every other method in terms of generalizability, with the lowest EF 1% of 21% being obtained for hdac2. This again highlights the ability of the DockM8 workflow to be customized to the target under consideration. These observations are also supported by the AUC-ROC and BEDROC metrics for which DockM8-max achieves median values of 0.891 and 0.653 respectively (see Supporting Information). It is worth noting that the removal of the RFScoreVS scoring did lead to an appreciable loss in performance for both DockM8-max-noRF and DockM8-best-noRF. This is not in itself surprising as a scoring function trained on most of the dataset would be expected to be a strong contributor to the best average combination of DockM8 conditions. However, we do note the significant performance increase afforded by our consensus methodologies relative to using the RFScoreVS scoring function on its own (which achieved a median performance of 22.17%). The five best (on average according to EF1%) DockM8 methods are shown in Table .