To carry out this assessment, the non-receptor tyrosine kinase TYK2 congeneric series was chosen as it is a challenging RBFE benchmarking set of sufficient size to allow reliable statistical analysis . The TYK2 series also involves a mixture of straightforward ligand sub-groups and more challenging perturbations that involve ring-changes. Additionally, the TYK2 series has been used recently in several RBFE works investigating network generation and machine learning potentials. For this series (16 ligands), RBFE was run for all possible perturbations in a single direction (n = 16 2 -16 2 = 120). Monodirectional edges were chosen with the purpose of halving computational cost. The signs of the relative binding free energy predictions for the 120 edges in this RBFE run were inverted to obtain the remaining 120 RBFE predictions, resulting in a bidirectional fully connected network with 240 edges. The validity of this assumption was supported by data generated during creation of the RBFE-Space training set (figure ). As stated previously, an ideal RBFE network generator will contain edges with low deviation from experimental measures. Thus edge scoring metrics that correlate more strongly with |∆∆G offset | values should select more accurate networks. To verify this, the statistical performances of available heuristics were compared to the |∆∆G offset | values (Eq 1) gathered from the fully connected TYK2 network. The data in Figure shows that ∆∆G bind SEM correlates well with |∆∆G offset |, therefore supporting the hypothesis that selecting edges with lower SF will lead to RBFE networks with lower errors. Figure shows that this correlation is maintained (albeit more weakly) with RBFE-Space SEM ∆G solvated values. Table summarises how different SF predictors correlate with offset values. As expected ∆∆G bind shows the strongest correlation, but this metric is computationally too intensive to be of practical use for network generation. Surprisingly edge scoring based on ECFP6 similarity shows no relationship with offset deviations. This is likely because the fingerprint is relatively insensitive to the different perturbations, with most edges assigned a similarity score of around 0.7. The inexpensive SF estimators RBFENN SEM and LOMAP-Score show comparable correlation with offset deviations. We note there could be many other reasonable proxy variables to the actual |∆∆G offset |, and future investigations may well identify descriptors with improved correlations.