As an initial case study, we use this new capability to optimize the parameters of a generalized Born (GB) 26 implicit solvent model. There are several advantages to working at this stage with an implicit solvent model rather than optimizing, e.g., LJ parameters in the context of explicit solvent. Firstly, using continuum electrostatics to approximate the interactions of atoms with water reduces the system size from thousands to hundreds of atoms, affording a ∼10x computational speedup, which allows us to include more hostguest systems in our training set. Secondly, prior computational studies of cyclodextringuest complexes have proven the applicability of host-guest systems to evaluate implicit solvent models. Thirdly, fewer parameters are involved in optimizing implicit solvent models than e.g., nonbonded force field parameters, due to the small number of GB atom types, so we have a favorable parameter-to-training data ratio. Finally, we note that the greater speed of absolute binding free energy calculations with a GB model means that an improved GB model that can reproduce binding affinities accurately would be invaluable for high-throughput virtual screening of candidate ligands for targeted proteins. We focus on the OBC2 form of the GB model, which was originally optimized to fit energies derived from finite difference solutions of the Poisson-Boltzmann (PB) equation. Though there are more advanced models, such as the GBneck and R6 effective radii approaches, and the AGBNP2 model, which adds further interaction terms, we selected the OBC2 model due to its simplicity, speed, and ready availability in the OpenMM simulation package. Here we report the optimization of GB cavity radii used in the OBC2 implicit solvent model against a collection of host-guest binding data and the evaluation of the resulting adjusted GB model against a larger test set of benchmarks comprising additional host-guest binding data, protein-ligand binding data, and small molecule HFEs. We find that the new GB cavity radii decrease the accuracy of the HFEs but provide excellent transferability to the test set binding free energies. This pattern of results traces to marked reductions in certain GB cavity radii by the optimization process. Our extended OpenFF-Evaluator infrastructure and the present case study provides a novel foundation for future work integrating host-guest binding data into the experimental data sets used to tune empirical force fields. We believe this approach will generate force fields that provide increasingly accurate protein-ligand Figure : Workflow of the ForceBalance-Evaluator framework for iterative optimization of force field parameters, θ, against physical properties. The baseline OpenFF-Evaluator can estimate and optimize force fields to experimental liquid-state properties and HFEs. Force-Balance can also interface with quantum mechanical software for the inclusion of quantum chemical reference data in the optimization (omitted from the diagram for simplicity). We have extended OpenFF-Evaluator to include the host-guest binding affinities, ∆G b , part of the workflow. The host-guest system definitions and experimental binding data are stored in Taproom, and our pAPRika binding free energy tool computes ∆G b and the gradient with respect to a set of force field parameters θ i (i.e. ∂∆G b ∂θ i ). OpenFF-Evaluator sends these quantities to ForceBalance, which returns updated parameters θ i+1 for a new iteration. In the diagram above, we only show OpenFF-Evaluator passing ∆G b and ∂∆G b ∂θ i to ForceBalance, though this is generalizable to the other properties as well. binding affinity predictions.