The main software that handle network generation internally are LOMAP (edge selection; as implemented in Flare V4), RDKit 2020.09.3 (molecular manipulation), networkx 2.6.3 (network manipulation) and matplotlib 3.4.3 with pydot 1.4.2 (network plotting). Similarities between networks (for the same congeneric series) were computed as the percentage of edge overlap between the two networks: given the number of overlapping edges, the percentage relative to both network sizes (n-edges) was computed. The mean percentage was taken as the final network overlap percentage. Given a set of RBFE predictions, the statistical performance versus experimental ligand binding affinities can be estimated. Whereas a per-edge ('pairwise') statistical analysis is meaningful, in this work a per-ligand free energy estimation is estimated using a weighted least squares regression method implemented in FreeEnergyWorkflows with weights set as the propagated standard error of the mean values across the replicates of each RBFE leg (solvated and bound) in kcal•mol -1 . Pearson R, Mean Unsigned Error (MUE) and Kendall τ metrics were estimated using a bootstrapping approach set to 10,000 repeats. Further plotting methodologies adhered to best practices. 3 Results and discussion