The production of some parameter sets with drastic changes that improve training set RMSE, but are not transferable, demonstrates that overfitting is a significant risk in parameter optimization. Typically, this risk is mitigated with regularization, penalizing solutions that stray too far from the initial solution. In this study, since we are interested in escaping local minima, we did not regularize the optimization; this led to the discover of some significantly improved parameters (lower values of 𝜖 for ether oxygens, higher values of 𝑅 𝑚𝑖𝑛∕2 for carbonyl oxygens) that represented much larger changes that what regularized optimizations produced. Results from multi-fidelity optimizations on mixture properties indicates that a more complex training target can also serve to constrain an optimization and improve transferability, while allowing parameters to vary considerably.