Effectively, this modification biases the search away only from areas where the classifier believes are infeasible. Put another way, all parameter space regions where P (feasible|x) ≥ 0.5 are treated as equally promising. We found that without using ρ(x), the search would often become trapped in parameter regions where P (feasible|x) ≈ 1, i.e. P (feasible|x) would overpower the α(x) containing factor and term in main text Equations 4 and 6, respectively. Validation of this claim is shown in Fig. , where we compare optimization performance of the minimum filtered and original versions of FIA and FWA functions using the 2d constrained HyperEllipsoid and Branin-Hoo surfaces. In each case, the minimum filtered version achieves a significantly lower regret value after 50 evaluations, while incurring a comparable number of infeasible measurements. Results of the discrete, ordered benchmarks are shown in Fig. and numerical values are summarized in Tab. S4. Results of the categorical, unordered benchmarks are shown in Fig. . Here, each run is terminated after identification of the surface's global optimum, and 100 independently seeded runs are executed. The major takeaways from the discrete, ordered benchmark tests are listed below.