Amongst recently proposed optimizers, q-Noisy Expected Hypervolume Improvement based Bayesian Optimization (qNEHVI-BO) is a state-of-the-art Bayesian optimization approach that caters to constraint handling, batch sampling and noisy evaluations. It was successfully implemented in a selfdriving lab for multi-objective optimization of thin films by MacLeod et al. . However, Low et al. demonstrated that the optimization procedure may lead to over-exploration, inevitably resulting in sampling wastage. We address this concern by introducing selection pressure from evolutionary algorithms (EA) as a secondary source of candidates, creating a hybrid framework hereby named Evolution-Guided Bayesian Optimization (EGBO).