This work introduces several new concepts to the field of RBFE. By grafting a large number of RBFE perturbations onto a common benzene scaffold, a transferable training set was created for RBFE research and development. As this set covers a diverse set of RBFE perturbations it is highly suitable for ML work and is set to drive research in combining RBFE and ML methodologies further. Using a siamese neural network architecture with graph representation of RBFE endpoint ligands, a statistical fluctuation predictor was trained on RBFE-Space. This SF predictor is shown to outperform state-of-the-art heuristics in the context of modelling SF. The prototype SF predictor (RBFENN) was used to generate the first ML-based networks for planning of RBFE calculations. The prototype data-driven network generators are shown to match performance of state-of-the-art rule-based RBFE network generators that have required extensive calibration over multiple years to perform adequately with specific RBFE implementations. By contrast the data-driven method offers full transferability to other RBFE implementations with the single requirement of running a set of prescribed RBFE-Space simulations to recreate SF values specific to that implementation. Beside network generation, the RBFENN SEM predictor presented in this work could be used to 'boostrap' adaptive sampling schemes for initial resources allocation. The availability of an inexpensive predictor of SFs could also be exploited by algorithms that sample chemical space to identify molecules whose RBFE reliability to a reference compound can be determined with ease. As all heuristics depicted in table 2 attempt to model |∆∆G offset | values, this begs the question as to whether a predictor can be trained directly on this quantity instead of statistical fluctuations. For this, instead of grafting perturbations onto benzene (as with RBFE-Space), the original ligands must be featurised as well as the protein system in which the RBFE perturbation takes place. This has been attempted before and offers additional information such as pose differences between input ligands which are highly influential to the RBFE reliability. However, the bottleneck in this scenario is that a large number of RBFE simulations must be run. Indeed, during early investigations of this work attempts were made to create a training set that included original ligands, but the chemical space associated with training such a model appeared too large with respect to the data available. For example, the PDBbind v2020 database 60 contains 19,443 protein-ligand complexes (with experimental binding affinities) across 5316 proteins. Assuming equal distribution of ligands per protein in this set brings the average size of congeneric series to 5316 ≈ 3.6. Mapping all edges in each network results in (3.6 2 -3.6) • 5316 = 49, 758 RBFE calculations, which is still a (very) conservative estimate as it is likely that some series will be larger than others: the number of possible edges in each series scales O(n 2 ). Alternatively, a retrospective dataset could be generated gradually using previously completed RBFE calculations. This in turn presents several challenges because each point in the dataset will need be standardised as in general RBFE protocols evolve over time (thus affecting the accuracy of the results for the same perturbation) and even within RBFE campaigns different edges may be allocated different degrees of sampling (e.g. different numbers of λ windows). Alternatively models could be trained on datasets built using ∆∆G bind SEM values (figure 5D and table 2): this would at least remove the requirement of experimental binding free energies for each data point, opening up the possibility of manually curating the chemical space in order to construct a diverse dataset rather than being restricted to congeneric series that have experimental data. Additionally, an RBFE-Space version with original ligands (i.e. not grafted onto benzene) would enable faster SF predictions as this removes the need for additional MCS calculations to map ligands onto RBFE-Space abstractions. However, this method would still require simulations of the bound leg for each data point (to generate the training set) which could still be prohibitively expensive. Training on ∆G solvated SEM values with original ligands is possible. However this space is still large due to chemical diversity of drug-like molecular scaffolds. We estimate that such dataset would require ∼2.5M perturbations. Another possible future direction is to pursue an active learning approach where the RBFENN is re-trained using newly-obtained |∆∆G offset | values for edges while a congeneric series is being explored in a drug discovery project. Overall this work has demonstrated the importance of perturbation network planning for RBFE calculations, and the potential of machine learning to automate the generation of optimal RBFE networks. Continued efforts in this direction will increase the robustness and effectiveness of RBFE methodologies for drug discovery.