Areas of high uncertainty according to QbC indicate areas of the configurational space that are not sufficiently described by the training set. Therefore, applying this to CSP landscapes we iteratively add predicted structures with high uncertainty to the training set. For the initial iteration, before the cNNP is first trained, we sample the candidates either randomly or by farthest point sampling (FPS). While ideally the model would be retrained after each new structure is added to the training set, this has a impractically high computational cost in most cases and thus we define a batch size for how often the cNNP is retrained, i.e. the number of structures added at each iteration of active learning. Another important parameter is the threshold for defining high uncertainty, σ, which determines the structures that are added to the training set. Setting this parameter depends on the desired accuracy of the final model, but is typically complicated by not knowing the relationship between the variance in the cNNP predictions and the true error a priori. Here we define the uncertainty directly as the standard deviation of the cNNP prediction and set an uncertainty threshold in terms of the target energy units, kJ mol -1 per molecule (abbreviated as kJ mol -1 hereafter). We find that a threshold of 1 -2 kJ mol -1 provides good accuracy for the systems studied. Candidates above the uncertainty threshold are added to the training set until either there are no more candidates or the percentage of candidates above the uncertainty threshold is below a specified target. Additionally, a maximum training set size can be set. The scheme is summarized in Figure (top panel).