While Fig. clearly illustrates how acquisitions from the eLCB and pure exploration functions relate to fea-tures in the landscape, it does not allow us to compare the convergence rate of the CA and PA strategies since the PA surrogate models use fewer data point than their CA counterparts. This is a consequence of how we defined the PA acquisition strategy, i.e., data is collected separately for the two targets and used to build independent surrogate models, hence the β-O-4 acquisitions are not included in the bottom row of Fig. . To obtain a better comparison for the convergence rates of CA and PA, we instead compare the CA surrogate models to corresponding PA models constructed from the entire PA dataset, i.e., including all the β-O-4 acquisitions (Figs. and). When comparing these landscapes, the differences between the strategies thus lie solely in the location of the acquisitions, rather than their number. The comparison reveals that CA does indeed yield a more accurate surrogate model, as evidenced by the fact that the PA model fails to predict the region surrounding maximum yield obtained for high P f and T . This can be attributed to the fact that in the CA strategy, new acquisitions always take the full set of previous acquisitions into account, even if they were made for another target, allowing for more informative points to be picked during both exploitation and exploration. We can thus conclude that CA is a more effective acquisition strategy than PA for BO-driven experiment design.