In contrast to the AgNP experimental campaign, we allow infeasible solutions to be evaluated rather than rejected, and no repair is performed. Thus, the optimizer learns the objective and constraint values in the infeasible space. This allows us to directly evaluate the ability of the optimizers to learn and consider constraints. We evaluate the optimization performance of qNEHVI-BO and EGBO, as well as pure U-NSGA-III and qNParEGO . Each computational experiment is run several times to extract statistical trends. To get a more accurate comparison to experimental campaigns, we perform MW7 optimization at 𝑛 = 8 variables, following a similar difficulty to other examples in literature which have a general range of 3-5 variables across different material systems , , , . Fig. shows the probability maps obtained for qNEHVI-BO and EGBO after 10 runs. EGBO is observed to be superior to qNEHVI-BO in exploiting the constrained PF, as it shows a higher probability density near the PF. This is confirmed by the logHV evolution in Fig. (top), where EGBO displays better optimization performance with a 28% lower logHV. While it manages to reach the true PF faster, this is done to the detriment of the sampling uniformity on the PF, as we see a 44% increase of the NU metric compared to qNEHVI-BO. Indeed, the probability density shows that the central part of the PF is more sampled than the sides. This can be explained by the bias introduced by the HV reference point ], as the PF extrema are associated with lower HV improvement.