To summarize, we here showcased the implementation of EGBO in a self-driving lab for the optimization of AgNP synthesis, a real-world cMOOP with 3 objectives and 2 complex constraints in a 5-dimensional decision space. The obtained results suggest that EGBO reaches the PF faster than qNEHVI-BO, with an early sampling of extrema solutions, leading to a wider Pareto front, although the spread of non-dominated solutions and the percentage of infeasible points are comparable for both optimizers. The experiments were terminated after 15 runs due to time and resource considerations.