The process of drug discovery has evolved from traditional iterations of trial and error synthesis of molecules and evaluation via assay to identify target binders. Even in modern drug discovery, getting a viable therapeutic or drug approved to help patients is costly both in terms of resources and time. Computational tools and software for computer-aided drug design (CADD) have seen an increase in application for early stage drug discovery campaigns to help circumvent these resource and time challenges. For example, quantitative simulation-based predictions of binding free energy (BFE) or physical properties are more commonly used and have shown increased promise to help select candidate binders and accelerate lead optimization by prioritizing which compounds to synthesize. Although BFEs are computationally demanding, recent successes, advancement in methods, and the increase in computational resources and power, it is foreseen they will have an increasingly important role for drug discovery. For BFE, such as relative biding free energy (RBFE) or absolute binding free energy (ABFE), predictions to be useful they must be accurate enough to help guide a drug discovery team's decisions. However, the accuracy of BFE calculations depends on several factors ( some described in ) including the accuracy of the chosen energy model and efficient sampling of the target (such as a protein) and ligand in the bound and unbound states.