For ECFPs feature sets, increasing the radius parameter allows for larger substructure information to be included. To test the effect of this, we build and compare models trained with ECFPs with larger radii, as shown in Figure -S22 and Table -S7. In traditional models, increasing the radius parameter does not yield higher MFD values with improved distinctions between the distributions of ground-truth and query data. On the contrary, for the DeepSVDD model, increasing the radius from 2 to 3 proves beneficial for generating an improved distribution to differentiate the ground-truth and query data with a higher MFD of 0.59, however the value of the MFD then decreased to 0.56 for yet higher radii.