In this section, we compare the CA and PA strategies in terms of the information content of their respective acquisitions. While the relative information content of acquisitions is difficult to quantify directly, we asso- Successive improvement of the surrogate models for the lignin yield (top) and β-O-4 content (middle) as new acquisitions (green circles) are added to the existing dataset (black circles). The panels display the surrogate models after adding batches 1, 2, 3, 4 (fitted with 9, 13, 17, and 21 data points respectively). As the landscapes evolve, the models' capability of predicting the yield in unknown regions of design space increases. The prediction uncertainties are quantified by the model standard deviation (bottom) which simultaneously decreases as more data is collected. The acquisition strategy balances exploitation of regions where the yield is large and exploration of regions with high uncertainty. ciate higher information content with faster convergence of the surrogate model. We carry out the comparison between the strategies by studying the batch-by-batch development of the landscapes, as well as the locations of acquisitions made with the eLCB and pure exploration acquisition functions.