While it is not feasible to directly identify the prediction uncertainty for all surrogate models, which is required for exploration, GP models were chosen due to their intrinsic ability to deliver variance for each prediction. To allow for a controlled trade-off between exploitation and exploration, different acquisition functions can be applied for sampling of subsequent datapoints. Within this comparison, the expected improvement (EI) acquisition function was chosen. Figure illustrates a comparison between exploitation and EI, starting with the same initialization. As shown, the differences between EI and exploitation become more obvious after the initial rise of the learning trajectory, with pure exploitation discovering the global optimum after more iterations. Figure -c provides insights into how the active learning algorithms explore the chemical space, where the graphs illustrate the true yield of every sampled condition, i.e., the experimental yield of a selected input parameter selection.