In summary, the optimal performance as judged by the MFD is achieved with 205-dimensional metal features and 256-bit ECFPs (radius=2) for traditional models and the LUNAR model. For DeepSVDD models, superior performance is observed with identical 205-dimensional metal features and 2048-bit ECFPs (radius=3). This discrepancy arises from the requirement of balanced feature dimensions for classic models, whereas deep-learning models benefit from an excellent capacity to extract pertinent information from extensive datasets, thus showing a preference for higher-dimensional and more precise features. Regarding LUNAR, despite its neural network-based architecture, its learning capacity and performance are highly related to the conventional distance-based k-nearest neighbour computation process, resulting in a shared feature preference with traditional models. Figure (a) lists the MFD value for models trained with these best-performing feature sets. To assess the stability and robustness of these models, 5-fold cross validation of MFD and OT values are implemented and shown in Figure (c). Additionally, the sole available assessment metric, TPR, is calculated and displayed in Figure . MFD and TPR can be used as complementary assessment methods for the OCC problem, depending on the specific objectives. A higher TPR indicates a greater proportion of actual positives are correctly predicted as positive, whereas a higher MFD signifies a smaller proportion of actual negatives are wrongly predicted as positives. High TPR and high MFD signify better model performance. In cases like ours, where there is a need to reliably differentiate the ground-truth positive data from predominantly negative query data, a higher MFD is a more important indicator. In addition, we perform a comparative analysis using randomly generated features with identical dimensionalities to the best representations for each approach, as shown in Figure (b) and Figure . Lower MFD values across all models trained with random features show that the domain knowledge captured in the 205 metal features and ECFPs is useful in making predictions.