We demonstrate BO of NPMs by efficiently searching an open database [ ] of ∼ hypothetical COFs, represented by vectors of hand-designed features based on domain knowledge, for those with the highest simulated methane deliverable capacity to store natural gas onboard vehicles [ ]. BO recovers the optimal (COFs) using fewer experiments than incumbent strategies including random search, evolutionary algorithms, and one-shot supervised machine learning. In the outlook, we discuss several active research areas in BO that are likely to apply to several problems in NPM discovery: (i) batch BO where experiments are parallelized, (ii) multi-fidelity BO, where NPMs can be evaluated using multiple methods which vary in accuracy and resource cost, (iii) multi-objective BO, where we aim to find a Pareto optimal set of NPMs to optimize multiple properties, and (iv) constrained BO, where our goal is to find high-performing NPMs which can be synthesized.