We must also select appropriate acquisition functions for sampling. Our aim is to fit surrogate models that are predictive over the entire design space, which necessitates a certain degree of exploration. However, we are also interested in quickly identifying extremal regions (minima or maxima) since many applications require, e.g., a high yield. To accomplish both these goals, we chose to perform two data acquisitions at each iteration: one data point suggested by the pure exploration function and one by the eLCB function. Using two different acquisition functions with two different experimental targets means that one BO iteration entails a batch of four experiments in total, which can be carried out in parallel in the laboratory to save time.