ing scaffold hopping, net charge adjustments or changes in ligand binding modes, as well as bottlenecks that are unique to certain implementations due to for instance shortcomings in supported empirical force fields. In RBFE the free energy of binding for a series of compounds is estimated from a set of pairwise binding free energy differences (∆∆G), which are transformed into binding free energies relative to a common reference value (∆G) via for instance a regression scheme. This requires the planning of a perturbation network (or graph) that connects all N compounds in a congeneric series using n edges. To connect all ligands to the network, at least n = N -1 edges is required (a minimally connected network), and up to n = N 2 -N 2 edges may be used (a fully connected network). Previous work has shown that accuracy of binding free energy estimation generally increases when the number of edges increases, but the computing expense of a fully connected network becomes rapidly impractical as the size of the congeneric series increases. If no error was made in the prediction of pairwise binding free energy differences (∆∆G), each possible network for a congeneric series would yield the same binding free energy estimates (∆G). In practice the choice of a network has a significant influence on predictive power, because a given RBFE protocol makes errors of a different magnitude for each edge. These errors arise from different sources that reflect fundamental limitations in the technology, for instance forcefield inaccuracies leading to systematic errors, and statistical errors that are introduced due to finite sampling of configurational integrals. Additionally, the performance of free energy difference estimation between pairs of compounds is influenced by numerous implementation specific details (e.g. softcore parameters, topological coupling methodology, λ schedule). Consequently the choice of a network that maximises accu-Fig. The choice of edges that define a perturbation network is essential for RBFE prediction accuracy. A: given two network generators (orange and blue, these can be humans or machines), B a number of perturbations is chosen between ligands in a congeneric series to form a connected network. For each chosen edge, an RBFE simulation is performed. Each edge produces errors of different magnitude. C: relative binding free energies are transformed using for example a regression scheme to obtain per-ligand ∆∆G bind estimations in reference to one of the series' ligands. D: compared to experimental binding free energies, different perturbation network topologies have different predictive power. In this example, the blue network outperforms the orange because the latter has multiple outliers.