Lead optimization is an early phase of the drug discovery process that simultaneously optimizes a hit molecule for potency, solubility, and other toxicological and pharmaceutical properties. Small modifications are made to the chemical scaffold of the hit molecule and tested for their effect on the properties of interest. A collection of such molecules, along with the initial hit molecule, is termed a congeneric series. Congeneric series developed within a drug discovery campaign can contain tens to hundreds of compounds, with the synthesis and testing of each chemical modification taking considerable amounts of time and money. Relative binding free energy (RBFE, also called ∆∆G) methods provide an in silico alternative to the labor intensive synthesis and experimental testing of each compound in a congeneric series. RBFE methods strike a balance between accuracy and throughput. Typical methods for RBFE determination utilize either molecular dynamics with alchemical perturbations or thorough sampling of the endpoints of the transformation. Alchemical methods, also called pathway methods, perturb the bound molecule from one ligand into another using chemical or alchemical means; Free Energy Perturbation (FEP) is one of the most popular alchemical methods. FEP utilizes explicitly solvated molecular dynamics or Monte Carlo simulations in which one ligand is alchemically transformed into another ligand. Recent advances in molecular mechanics force fields, sampling, and reductions in computational cost have encouraged the adoption of the FEP approach for the prediction of RBFE in both academia and industry. These advances have allowed for very high accuracy of FEP approaches, within about one kcal per mol. However, the current implementations only allow for the calculation of about four ligand perturbations per day with commonly available computing resources. FEP is somewhat limited to a maximum number of changes between ligands, about 10 heavy atoms, due to the high amount of sampling required for each change of a ligand. However, with careful consideration, more heavy atoms can be changed between the ligands while still achieving low errors in predictions. Endpoint sampling methods reduce the required amount of molecular dynamics needed to determine the free energy of the system. The most popular methods for determining RBFE with endpoint sampling are molecular mechanics Poisson-Boltzmann Surface Area (MMPBSA) and molecular mechanics generalized Borne surface area (MMGBSA). MMBPSA and MMGBSA, developed by Kollman et al., evaluate the free energy through molecular dynamics simulations of the unbound ligands and the bound complexes. RBFE is computed via a simple difference of the energetics in each of the ligand binding modes. While these methods have reduced computational requirements in comparison to FEP, their free energy predictions are not as rigorous. This limited throughput of molecules and low allowance for changes between molecules can prevent medicinal chemists from fully exploring the optimization space of a lead molecule.