the cost of early-stage drug discovery. Part of such a computational toolbox are alchemical perturbation methods. They provide an effective strategy for computing free energies of binding between proteins, or proteins and ligands. With both open source and commercial tools available, these methods have been widely adopted across both academia and industry. Particularly relative binding free energy (RBFE) methods can provide reliable and accurate results with high-rank correlations (R > 0.7) and low root mean square errors (RMSE) (< 1 kcal/mol) in free energy calculations with respect to experiment for many protein systems. RBFE methods have seen wide adoption for free energy prediction in lead optimization, particularly in combination with active learning strategies, to drive down computational costs. However, some technical challanges, as well as system dependen ones remain. For example, despite the wide usage of soft-core potentials, handling singularities arising from appearing or disappearing particles remains an active area of research. As an alternative to soft-core potentials, neural network potentials promise to alleviate singularities from softcore approaches when transforming from one ligand to another using single topologies. Accounting for charge changes in transformations between two ligands, or how to most accurately describe a variety of ligands and proteins also leaves room for improvement.