BO is an active search method to find the optimal NPM in a data set whilst evaluating, with some expensive method such as a molecular simulation, only a small fraction of the NPMs in the data set. BO achieves this by making acquisition decisions that take into account all past observations and balance exploration and exploitation. The adoption of BO could dramatically impact high-throughput computational screenings of NPMs by reducing the computing cost of finding the optimal NPM, allowing us to screen larger databases of NPMs, and enabling the use of higher-fidelity but more expensive molecular models and simulation methods. Notably, BO applies to NPM search in the experimental domain as well.