We focused on two separate optimization tasks, both initially developed in our previous study . Both tasks optimize the same set of 12 LJ parameters (𝑅 𝑚𝑖𝑛∕2 and 𝜖 for 6 LJ SMIRKS types), and both use the same small molecules (alkanes, alcohols, ethers, esters and ketones) in the their training sets. The tasks are differentiated by the different types of physical property training data that are used in the evaluation of the objective function: While we reported optimized parameter sets for these tasks in our previous work, here we use those results as a baseline to test our multi-fidelity strategy.