A longstanding challenge in computational drug discovery has been the development of methods for efficient in silico prioritization of compounds. In recent years, relative binding free energy (RBFE) calculations have become the gold standard for accurately computing binding affinities , a key property during small molecule optimization. However, these simulations remain complicated to set up, computationally expensive to run and technically challenging to scale to large sets of compounds. In general terms, a standard FE calculation workflow first requires generation of a plausible low-energy conformer for each compound from a set of congeneric binders, ideally with consistent formal charge. The resulting embedded ligands are then docked into a protein crystal structure, typically requiring constraints or hand-modelling to generate acceptable poses similar to that of a known binder. Atoms for perturbation are then mapped between pairs of proximate compounds (based on compound similarity), and a relative change in binding free en-ergy is computed for each edge. Typically, around 60 ns of total simulation time is required for a single perturbation .