To show the generality of our results, we performed additional studies where the EA U-NSGA-III in EGBO is replaced first by Multi-Objective Evolutionary Algorithm based on Decomposition with Improved Epsilon (MOEA/D-IEpsilon) (see SI 3.2 and 3.3). We report the results of these variants alongside other constrained multi-objective algorithms on various synthetic cMOOPs. Similar performance is obtained when using U-NSGA-III or MOEA/D-IEpsilon in EGBO framework. We therefore confirm that the good performance obtained with EGBO is mainly due to the integration of exploitation from selection pressure (U-NSGA-III or MOEA/D-IEpsilon) with the explorative nature of qNEHVI-BO.