Comparing the models trained using different metal features, most of those employing fewer features exhibit inferior performance in distinguishing between ground-truth and query . A comparison of the MFD values of the models trained using three different dimensional ECFPs is shown in Figure . The improved performance of the DeepSVDD model with higher-dimensional input can be attributed to the feature compression and sample reconstruction benefits provided by the autoencoder. Overall, it is evident that a feature space which is more balanced between metal and linker features generally results in improved score distributions in traditional models, minimising the overlap between the allpositive ground-truth dataset and the query dataset that includes a substantial but unknown number of negative samples. Traditional models and the LUNAR model exhibit their optimal performance measured by MFD when utilising 205-dimensional metal features and shorter 256-bit ECFPs, resulting in MFD values of 0.50, 0.48, 0.36, 0.44, 0.47, and 0.56 for IForest, KNN, OCSVM, LOF, CBLOF, and LUNAR, respectively. However, the DeepSVDD model demonstrates improved performance with identical 205-dimensional metal features and longer 2048-bit ECFPs, achieving an MFD value of 0.57.