Active learning is purely based on exploration to enable as accurate as possible predictions of the design space. Oppositely, Bayesian optimization balances both exploration and exploitation for finding the optimum in the design space and treats every iteration as the potentially final one. Exploitation investigates areas with a high objective value to find an optimum nearby whereas exploration discovers areas on which the predictions are unknown and therefore uncertain. Exploration requires a measure of uncertainty on the predictions to identify which areas of the design space remain unexplored . Therefore, popular machine learning models for active machine learning are Gaussian processes and Bayesian neural networks as these allow an uncertainty estimation of their predictions. Neural networks can also be employed for active machine learning purposes, but approximative methods such as Monte Carlo dropout or model ensembling are required to estimate the model uncertainty .