In the following, we propose to evaluate EGBO performance on various constrained multi-objective optimization problems (cMOOPs), in a real closed-loop experimental self-driving lab (see Fig. ) as well as with synthetic data. We custom-built this self-driving lab to solve a silver nanoparticle (AgNP) synthesis optimization problem, requiring simultaneous achievement of three independent objectives, with five independent input parameters and three complex constraints (see Fig. , right). EGBO performance was tested, in parallel with qNEHVI-BO, on three different metrics evaluating its ability to reach the PF, sampling uniformity on the PF and sampling wastage. We also introduce synthetic datasets with scalable multi-objective problems of varying complexity to show the generality of our algorithmic approach. We successfully demonstrate that EGBO outperforms qNEHVI-BO in terms of optimization efficiency and constraint handling. We show that selection pressure helps EGBO to bypass infeasible regions located near the PF, even in high dimensional decision spaces with very limited feasible space. We finally perform studies to explore how the framework could be adapted to further improve constraint handling and comment on its wide applicability in the physical and chemical sciences.