Here, we have taken the narrow view that the optimal ABFE workflow would produce minimal overall uncertainty in all free energy estimates for a fixed computational cost. However, practitioners in industrial drug discovery may not need precise estimates of affinities for weak binders, so long as they are correctly identified as weak. It is also likely more useful to accurately predict the affinities of dissimilar ligands with distinct receptor interactions than more similar ligands for which ∆G o Bind may be predicted with more confidence using a machine learning model trained on previous ABFE results. Future work may take a more realistic view of the "optimal" workflow, for example by quickly terminating simulations which show weak binding. This is likely to be robust because, as we have discussed, unequilibrated free energy estimates tend to overestimate affinity. Tighter coupling between free energy protocols and active learning protocols may also be useful -for example, the number of replicas and simulation time may be increased in proportion to the uncertainty of a machine-learned predictor of affinity.