The blue curves in among the acquired set of COFs, X n . The second and third metric are, among the acquired set of COFs X n , (b) the highest deliverable capacity rank, with the rank defined using the entire data set X, and (c) the fraction of the COFs in X with the highest deliverable capacity. % of the BO searches acquired the optimal COF x * after n = 120 COF evaluations; all BO searches acquired the optimal COF after n = 174 COF evaluations. After n = 250 COF evaluations, BO acquires % of the top COFs in the data set. This illustrates how BO can provide a simulation-efficient search for the optimal COF as opposed to conducting an exhaustive search; BO picked out the top COF in the database of ∼ COFs while evaluating less than ! BO also outperforms random search, evolutionary search, and the one-shot supervised learning approach. With the same number of COF evaluations (n = 250), a random search (on average) acquires only the th ranked COF and . % of the top COFs. The performance of random search is poor because it ignores the past observations when it selects the next COF to evaluate. Evolutionary search and one-shot supervised learning (diverse training set) have a much better search performance than random search, acquiring the th/ th ranked COF and the top %/ % of the top COFs on a budget of evaluations. Though, evolutionary search nor the one-shot supervised learning strategies recover the optimal COF x * after evaluations. Thus, BO outperforms the baseline search methods of evolutionary search and one-shot supervised learning using both metrics of (a) the highest deliverable capacity in the acquired set and (b) the fraction of the top COFs in the acquired set given a budget of evaluations. N.b., BO is designed to optimize the former performance metric, but BO could be tailored to optimize a top-k metric .