Bayesian Optimization (BO) methods have emerged as a popular and efficient approach to address such tasks . Bayesian approaches are generally employed for optimizing so-called "black-box" functions, where the fitness function and its derivatives are unknown or too expensive to evaluate. In the context of a closed-loop DMT environment, function evaluation corresponds to the actual chemical synthesis of a new compound and its subsequent biological evaluation (e.g., binding affinity determination). Reducing the cost and time taken to complete multiple rounds of design, make, and test is of paramount importance. To achieve this, the biological system of interest is approximated by a computational model (surrogate) based on previous results. This computational model is then queried to predict the outcomes of possible experiments, proposes new sets of experiments likely to succeed, and is iteratively refined after each DMT cycle, increasing its prediction accuracy over time.