involving alkyl-OH motifs. This has been subsequently corrected by tuning softcore parameters. These new parameter settings have not been used for the generation of the current version of RBFE-Space which explains the behaviour of the data-driven approach in this analysis. Although the main aim of the TNKS2 screen was to compare directly the performances between RBFENN and LOMAP-Score networks, SOMD performance on TNKS2 in this work is poor compared to results published elsewhere. For example, Schindler et al and Gapsys et al report MUE values of 0.62 and 0.73 kcal•mol -1 , respectively. Note that these values were computed using edges on neutral ligands only. Both of these examples contained considerably larger RBFE networks (n=45); Schindler et al note that these were obtained by requesting an optimal topology from the FEP+ implementation and no manual network augmentation was performed. This suggests that increased performance could be have been achieved using networks with a greater number of edges. Indeed, in-house results from Cresset suggest that Flare FEP (which deploys SOMD as its back-end RBFE engine) outperforms per-ligand binding affinity predictions of Schindler et al with a manually adjusted network (n=70 edges), giving a MUE of 0.60 kcal•mol -1 and a pearson R value of 0.75 (tables S1 and S2).