These results demonstrate that we can find improved parameter sets using multi-fidelity global optimization, but that care must be taken to avoid overfitting. We performed 5 optimization runs that all significantly improved the objective, but with large variations in the parameter vector solutions. There are a wide range of parameter vectors that are able to satisfy this optimization problem, but their transferability is not guaranteed. This is probably due to the training set which was chosen, as the set contains 56 target points and 12 parameters; additionally, the parameter set is "segmented" in that some parameters and targets are independent from the other parameters/targets. For example, the [#8X2H0+0:1] (ether oxygen) parameters are only dependent on the measurements of 𝜌 𝐿 and Δ𝐻 𝑣𝑎𝑝 for ethers. This leads to an optimization where overfitting is a significant concern. We could address overfitting by using a regularization scheme, as many others have done, but this many prevent us from escaping local minima in parameter space, as we hoped to do.