While one-shot supervised learning does not constitute active search like BO, it is the most popular method for circumventing an exhaustive search for the optimal NPM using the high-fidelity evaluation method. One-shot supervised learning has three stages. ( ) COFs are selected and evaluated to serve as training data for the supervised learning model. ( ) The trained model is used to predict the deliverable capacity of the remaining COFs. ( ) We evaluate the COFs with the highest predicted deliverable capacity. Stages ( ) and ( ) both incur (costly) COF evaluations. We compare one-shot supervised learning and active search by comparing the deliverable capacities of their acquired set of COFs when given the same budget of COF evaluations. Much like balancing exploration and exploitation, we elect to split the budget of evaluations for oneshot supervised learning among stages ( ) and ( ) equally. More, for stage ( ), we try two training set acquisition strategies: (i) uniform random selection of COFs and (ii) a max-min diversity selection strategy [ , ], whereby we sequentially acquire COFs for the training set, selecting the COF with the maximum minimum distance (in COF feature space) to a COF already in the training set (starting with an initial, random COF).