We hypothesize that edges in a RBFE network with low statistical fluctuations are associated with low |∆∆G offset | values. This hypothesis reflects the empirical observation that, for a given protocol, RBFE predictions with large statistical uncertainties rarely give accurate estimates of experimental measures. Of course an RBFE edge prediction with a low SF could significantly deviate from the experimental measure due to systematic protocol errors (for instance due to a poor description of the energetics by the chosen forcefield), but as long as a reasonable correlation is observed, networks selected according to this metric will approximate the optimal choice. The chief motivation for this assumption is that it only requires estimation of the SF of edges for a given RBFE protocol, which can be done without knowledge of the experimental measure. Later we will show that this hypothesis is supported by data. However estimating SF values for every given possible edges in a network via for instance calculation of the standard error of the mean binding free energy change (∆∆G bind SEM) would be impractically time-consuming. Our task is therefore to find a descriptor that approximates ∆∆G bind SEM and that can be inexpensively computed to plan an RBFE campaign. To do so we turn to machine learning (ML) and subsections 2.1-2.3 outline the associated methodological steps (training set generation, model training, and model applications). ); 2) the SF of the perturbation is estimated by computing free energy changes in an simplified environment. Various representations were considered (e.g. vacuum) and an aqueous phase environment (section 2.1.2) was selected to offer a condensed phase description at a computationally affordable cost.