Molecular dynamics (MD) simulations with empirical force fields are a valuable tool in the field of computational chemistry and biophysics. MD simulations allow us to probe proteinligand systems at the molecular level and estimate physical quantities of interest at a lower cost than experimentation and more quickly and with better conformational sampling than quantum mechanical methods that explicitly treat electronic degrees of freedom. A quantity of particular interest in computer-aided drug design (CADD) is the affinity, or binding free energy, of a targeted protein with a candidate small molecule ligand. This quantity can be computed with MD simulations, but these calculations remain time-consuming and error-prone. The errors can arise from a number of sources, including uncertainties in the protonation states of the protein and ligand, both before and after binding; the difficulty of establishing a complete and suitable initial protein structure with a sufficiently accurate ligand pose; the challenge of achieving adequate numerical convergence of slow protein motions; and approximations made by the force field in modeling the potential energy surface. This array of error sources makes it difficult to isolate or attribute discrepancies from the experiment to any one source. Aqueous host-guest systems embody much the same physical chemistry, such as conformational changes upon binding, hydrogen bonding between host and guest, and hydrophobic interactions. However, they are much smaller and simpler than proteins, enabling faster convergence and posing fewer ambiguities in protonation states and structures. For these reasons, host-guest systems have been used to test the accuracy of binding affinity calculations in the SAMPL blinded prediction challenges and to evaluate empirical force fields in retrospective studies. Indeed, the force field is a critical determinant of the accuracy of a binding free energy calculation. Empirical force fields are typically tuned to fit quantum mechanical (QM) chemical data and/or liquid-state physical properties. There are also recent efforts to fit empirical force fields to small molecule crystal structures. However, curating the experimental data and organizing them into a uniform format for use in testing and tuning force fields can be laborious. In addition, it is a non-trivial task to optimize force field parameters to many physical properties simultaneously. The Open Force Field (OpenFF) initiative has addressed this challenge by creating OpenFF-Evaluator, a software framework that provides an automated and highly scalable interface to estimate multiple physical properties, such as densities, dielectric constants, enthalpies, and hydration free energies (HFE). In addition, OpenFF has curated experimental data for these physical properties from the National Institute of Standards and Technology (NIST) ThermoML 17 and FreeSolv databases.