Prioritizing the synthesis of compounds by means of computationally predicted binding affinities has become one of the central strategies in small molecule drug discovery. There are different methods ranging from data-driven artificial intelligence to more rigorous physics based models. Among the latter, the calculation of relative binding free energies (RBFE) from alchemical molecular dynamics (MD) simulations is probably the most frequently used and accurate method given the accessible time scales for the size of the ligand-protein complexes. RBFE calculations involve alchemical perturbations, where a ligand is changed into another, via a chemically unrealistic pathway. This can only be achieved in silico such as by changing atoms of one element into those of another. Following the alchemical pathways across the thermodynamic cycle will result in the same double free energy difference for the perturbation in solvent and protein as when traversing the physical pathways, i.e.