We emphasize the role of exploratory acquisitions in obtaining this level of surrogate model accuracy over all of design space despite using small dataset consisting of only 21 experiments. An additional benefit of promot- To assess the accuracy of the models' predictions, the measured target values for a set of independently collected test data is used (right). The predictions for the test set are contrasted to the predictions for the set of acquisitions used to train the models (left). The error bars indicate the predicted standard deviations. Both models provide accurate predictions that are comparable across the acquisitions and test data, indicating that the models strike a good balance between generalizing to new data and reproducing the training data. ing exploration is that the final dataset is better suited for fitting surrogate models for other targets that were measured during the BO-guided data collection, since exploration, unlike exploitation, is not specific to a target function. While the added emphasis on exploration impedes the convergence of the landscape maxima, we still observe that changes in the predicted maxima are small between the third and fourth batch.