We explained the key ideas behind Bayesian optimization (BO) and advocated for its use to efficiently search databases of NPMs for the one with the optimal property, whilst synthesizing and evaluating the fewest NPMs. The ideas of BO, to sequentially, actively make intelligent decisions on which NPM to synthesize and evaluate based on the past experiments, can be applied to both the laboratory (driven by humans or robots ) and computational settings. The two core ingredients of BO are ( ) a surrogate model that approximates the structure-property relationship and describes our uncertainty in it and ( ) an acquisition function that scores the utility of evaluating each NPM next, designed to balance exploration and exploitation. We demonstrated BO of NPMs by using BO to search through a database of ca.