Our goal was to efficiently search a database of ∼600 COFs for the one exhibiting the largest adsorptive Xe/Kr selectivity. We had access to two molecular simulations of Xe/Kr adsorption to predict the selectivity of a COF: a high-fidelity binary grand-canonical Monte Carlo simulation and a low-fidelity Henry coefficient calculation with a smaller runtime. We employed multi-fidelity Bayesian optimization (MFBO) to orchestrate the sequential search for the COF with the largest high-fidelity Xe/Kr selectivity. MFBO constituted an iterative feedback loop of (1) conduct a lowor high-fidelity simulation of Xe/Kr adsorption in a COF, (2) use the simulation data gathered so far to train a surrogate model that predicts the selectivity of COFs, according to both low-and high-fidelity simulations, based on their structural and chemical features, with quantified uncertainty, then (3) choose the COF and fidelity for the next simulation via maximizing an acquisition function that balances exploration, exploitation, and cost. MFBO acquired the optimal COF among the ∼600 candidates, having the largest high-fidelity Xe/Kr selectivity, using only 38 low-fidelity and nine high-fidelity simulationsincurring only 5% and 22% of the average runtime to find the top COF via random sequential search and single-fidelity BO, respectively. Visualizing the location of the acquired COFs in the design space as the search proceeds reveals that MFBO judiciously planned the sequence of simulations to balance exploration, exploitation, and the cost of the two types of simulations. Similar in spirit to multi-fidelity machine learning and two-stage search, the cheap-, low-fidelity calculations of dilute adsorption properties could serve as features (inputs) to a supervised machine learning model to predict the high-fidelity adsorption property.