In this section, we conduct a further experiment with the intent of decoupling our results from the use of the archives' data (MPDS and MP) as our resource for gathering additional data. In fact, the use of an external database does not guarantee that the experimental conditions in A are met in B, which can lead to heavy distribution shifts . Instead, here, we simulate a progressive data acquisition of one single dataset. This is done by initially constricting dataset A to a random subset comprising only 5% of the original size. Subsequently, DiSCoVeR is used to integrate new candidates from the remaining 95% of itself. Similarly to the previous experiment, the novelty scores are updated and the new data points are iteratively added until the whole dataset is exhausted. The aforementioned strategy is compared with a random acquisition which iteratively adds random data points ignoring any novelty constraint. We assess the outcomes of the self-acquisition process on top of a test set created by holding out an amount corresponding to 20% of the original dataset: in one case such test set is created randomly; in the other case the DiSCoVeR algorithm is utilized to construct a biased test set with proportionate representatives of ordinary and extraordinary materials, with proportions 2/3 and 1/3. The primary objective is to evaluate whether a biased data acquisition approach facilitated by DiSCoVeR enhances the discovery of these new stoichiometries.