With the advent of GPU computing, MD TI simulations became feasible for highthroughput drug discovery applications; however, computing BFEs for a large number of ligands remains technically difficult, costly and time consuming. The approach suffers from various technical difficulties, including tedious system setup and preparation; inaccuracies in modelling difficult transformations such as ring breaking/forming, ring extensions, and changes in the net charge; and the difficulty in achieving sufficient sampling across all intermediate states. Combined, these problems often prove unsurmountable, leading to the selection of cheaper and simpler methods with inferior accuracy. Simplified computational methods, e.g. molecular docking, a commonly used computational screening technique, can quickly yield reasonable ligand binding poses; however, its scoring functions are typically insufficiently accurate for discriminating false positives from true hits, which is a serious problem when thousands of potential hits have been proposed with limited resources for experimental validation. End-point MD methods, such as MM/PBSA and MM/GBSA, may sometimes afford limited accuracy improvement when compared to docking, albeit at a greater cost; yet their accuracy is strongly system specific and is generally less reliable than MD TI simulations. Recently, we have developed workflows for high throughput ABFE and RBFE calculations for small molecules. A hit-to-lead optimization framework combining RBFE simulations and active learning (AL) was designed to iteratively explore large chemical spaces consisting of thousands of congeneric molecules for high performing molecules when compared to an experimentally validated reference compound. In this scheme, batches of molecules are iteratively selected by a machine learning (ML) model trained on previously performed RBFE calculations and a featurization of the respective molecules, with an overall goal of harvesting the molecules with the most negative ∆Δ𝐺 𝐴→𝐵 𝑏𝑖𝑛𝑑 value. In initial iterations, molecules are sampled in an exploratory manner with focus being areas of the chemical space where the ML model is most uncertain. Latter iterations sample in an exploitative fashion, where the molecules selected are predicted to have the most negative ∆Δ𝐺 𝐴→𝐵 𝑏𝑖𝑛𝑑 value. After RBFE calculations are performed on a sample, it is used to augment the training data for further AL cycle iterations. With this framework, we were able to explore a chemical space consisting of 8715 ligands with only 253 simulations, identifying 133 compounds with a higher predicted affinity. Others have also demonstrated the active learning framework efficacy in RBFE simulations, as well as in structural docking screeening, or in forcefield development and coarse-graining. Even with our active learning framework, the computational cost of requisite simulations requires significant resources, often in the thousands of GPU hours. ABFE simulations have been proposed as an accurate and cost-effective method for the final stages of high-throughput virtual drug screening for initial hit discovery. Beginning with large datasets of molecules, often in the millions to billions, this approach utilizes high-throughput docking to narrow down the set of candidates to a number feasible for ABFE simulations, typically in the hundreds or thousands, with top performing candidates submitted for experimental validation. The utility of combining docking with alchemical BFE simulations has been recognized for several decades due to their complementary nature: the inaccuracy of docking can be corrected with ABFE rescoring, while docking both significantly reduces the number of ABFE simulations required and provides reasonable starting poses. Still, this may involve thousands of tedious and expensive calculations, which can be infeasible to perform. Thus, both RBFE and ABFE simulations would benefit from methods designed to lessen the cost of such high-throughput computations.