Fig. shows the outcomes of the A-A aggregation process in the case of the bulk modulus, which is representative of the results observed across all datasets. As for the regression model, we limit here, for brevity, our presentation to the two SOTA for classic ML and DL methods, i.e. CrabNet (left) and random forest (right). The figure encompasses the two exposed test scenarios: the randomly selected test set (top) and a biased test set created using the DisCoVer algorithm (below). Notably, our analysis uncovers a consistent pattern across both test configurations. Contrary to our initial expectations, in both cases, where the tests are either random or biased, the application of DisCoVer-guided data acquisition leads to a deceleration in the model learning process with respect to a random acquisition strategy. This observation holds true for both CrabNet and the random forest model, though with a different intensity. These findings underscore an intriguing phenomenon: the incorporation of bias, even when guided by the DisCoVer algorithm, appears to impede the learning progress of the models. Furthermore, this suggests that the balancing of a dataset in terms of chemical diversity is not to be thought of in correspondence with better ML accuracies. Consequently, a thorough examination of the intricate interplay between data acquisition strategies, model architecture, and test set composition is warranted with the intent of gaining deeper insights and devising more effective approaches for model training and evaluation in the field.