For this application, we observe that the naïve-ignore, FWA, and FCA-0.2 strategies need to traverse the smallest fraction of the parameter space until identification of the inhibitor with the minimum IC 50 , at 7.6 ± 0.5%, 8.0 ± 0.5%, and 8.3 ± 0.5%, respectively. Compared with the HOIP application in the previous subsection, which features 92% infeasible options, this application features only 21% infeasible options. This difference is reflected in the best performing strategies for this application. It appears that for categorical applications characterized by a small infeasible fraction, it behooves one to use strategies which are less risk-averse when it comes to violating the constraint function. In fact, naïve-ignore is among the most efficient search strategies, and completely turns off the feasibility classifier contribution in Eq. 6. However, a more risk-averse strategy, such as FCA-0.8, measures infeasible points at a markedly lower rate (19.9±1.1) but does so at the cost of roughly double the amount of evaluations needed to find the optimum. Researchers should thus exercise balance between optimization performance and constraint violation risk aversion in their selection of feasibility aware acquisition function.