Raw scores predicted by different fitted models indicate the dissimilarity between input query samples and the ground-truth dataset (here, these are the metal and linker combinations in the 1M1L3D dataset) with low scores representing a high probability that an unknown sample belongs in the positive class. In this study, these scores are multiplied by -1 and normalised within the range of [0,1] so that lower scores indicate metal linker pairs which are unlikely to be synthesisable while scores approaching 1 indicate metal linker pairs which are more likely to be synthesisable. We adopt the hypothesis that in a typical OCC problem, the ground-truth dataset comprises exclusively positive samples, whereas a significant proportion of the query dataset consists of a substantial number of true negatives. Therefore, fitted models trained using the ground-truth dataset containing solely positive samples are expected to assign higher scores on average for the ground-truth data and lower scores for a significant proportion of the query data. Given the task of scoring samples in the query dataset to predict which samples are likely to belong to the ground-truth class, a superior model should result in different distributions of scores for the ground-truth dataset and the query dataset. A score distribution for a poorly discriminating model and a successfully discriminating model are illustrated in Figure . A superior model should give lower scores on average to the query samples, resulting in a larger separation between the ground-truth (orange) and query (blue) distribution areas. Ideally, the overlap (grey) between these datasets should be small, allowing the identification of a reliable area with a set of query samples which are confidently predicted to lie within the ground-truth class. An inferior model in which the query and the ground-truth data are given very similar distributions is unhelpful, as it cannot provide confident predictions of which query samples are likely to lie in the ground-truth class. In this case, the model tends to predict everything to be positive and therefore get a TPR close to 1. To quantitively assess the discriminative performance of different models after being trained, we propose the use of the Maximum Fractional Difference (MFD). The fraction of groundtruth (orange) and query (blue) datasets that would be classified as positive can be calculated by setting the threshold to a specific normalised score for each model (Eq. ( )). The fractional difference (Eq. ( )) between these two datasets, as depicted by the red curve in Figure , quantifies the discriminative capability under particular thresholds. The MFD corresponds to the maximum value (Eq. ( )) of this curve, with superior models demonstrating higher MFD values. As shown in Figure (b), the plot depicts fractions of both datasets for the poor model (left) and the successful model (right) as a of the normalised score threshold, T, ranging from 0 to 1. The inherent characteristics of ML models, including their capacity to generalise and adapt to noise in the training data, means that not all ground-truth data will be given a normalised score close to 1. In such a case it is likely that ground-truth samples with normalised scores in the low score tail of the distribution represent a region of normalised score in which the model is not reliable in predicting true positive datapoints. For example, this may be due to entries in the ground-truth set which are in some way sufficiently dissimilar to other entries that a statistical model is unable to recognise that this entry should be labelled correctly. To discriminate between entries which are likely to be reliably predicted to be true positives and those which are not, we define the optimal threshold (OT) as the normalised score at which the MFD is achieved (Eq. ( )). This threshold delineates the boundary between the reliable and unreliable regions for the predictive performance of the model, where entries in the query dataset with normalised scores above OT can reliably be predicted to have positive labels.