We assessed the performance of the refit force fields on the test set. Plots of RMSEs for the four types of physical property data in the test set are shown in Figure . Between the simulation-only and multi-fidelity optimizations, performance is similar on pure and mixture densities (𝜌 𝐿 and 𝜌 𝐿 (𝑥), and not significantly improved when compared to OpenFF 1.0.0; densities are already well-predicted in OpenFF 1.0.0. For Δ𝐻 𝑣𝑎𝑝 and Δ𝐻 𝑚𝑖𝑥 , the multi-fidelity optimizations significantly improve both properties, whereas the simulation-only optimization only improved Δ𝐻 𝑚𝑖𝑥 . The 𝑁 = 20 optimization has a Δ𝐻 𝑚𝑖𝑥 RMSE of 0.24 kJ/mol (95% CI 0.22, 0.26), similar to 0.25 (0.23, 0.27) for the simulation-only optimization. For Δ𝐻 𝑣𝑎𝑝 , the 𝑁 = 20 optimization has an RMSE of 4.83 kJ/mol (3.75, 5.82), significantly improved over the simulation-only optimization value of 7.87 kJ/mol (6.61, 9.14), which has slightly regressed prediction of Δ𝐻 𝑣𝑎𝑝 compared to the original force field. It is notable that we are able to achieve significantly improved performance on both types of enthalpy data in the test set, indicating that parameters found with the multi-fidelity optimization process achieve better transferability than a simulation-only optimization against the same data set. Examining the test set results for the 𝑁 = 20 run separated by functional group, as shown in Figure , helps to better understand how parameter changes influence force field performance. A notable result is that esters and ketones perform better on Δ𝐻 𝑚𝑖𝑥 and Δ𝐻 𝑣𝑎𝑝 in this refit force field compared to their performance with the simulation-only refit. This may be due to changes in the parameters (bigger increases in 𝑅 𝑚𝑖𝑛∕2 ) for the [#8:1], which is exercised only by carbonyl oxygens in both the training and test sets. However, this parameter change has an interesting effect on densities; while ester densities and densities of ester-containing mixtures are largely improved, the ketone densities regressed significantly (both with multi-fidelity optimization and simulation-only optimization). This, along with parameter gradient evidence from our previous work, suggests that splitting LJ types responsible for ketones and esters may yield improved prediction of densities.