We present a new approach for large-scale optimization force field parameters against physical property data, based on equilibrium simulations and Gaussian process surrogate modeling. Our multi-fidelity strategy uses an iterative process of global optimization over the cheap surrogate surface and validation performed at the simulation level. We demonstrate that for reasonably sized sets of physical property data, multi-fidelity optimization can find improved parameter sets while exploring more widely than traditional local optimization techniques. Particularly, we find that larger changes for parameters changes related to ethers and carbonyls yield transferable improvements, improvement test set measurements of both Δ𝐻 𝑣𝑎𝑝 and Δ𝐻 𝑚𝑖𝑥 when training against binary mixture data; training against the same dataset using only local simulation-based optimization was able to achieve comparable improvements on Δ𝐻 𝑚𝑖𝑥 , but not Δ𝐻 𝑣𝑎𝑝 . Through examination of the training and test data, we are also able to identify targets for parameter splitting.