We present two applications of CKA for molecular embeddings. First, we give an intuition as to why optimizing fingerprint radius only leads to minor changes in performance (as reported in earlier work 4 ). Secondly, we show that embeddings created from GNNs with different readout operations can be differentiated by CKA. The second set of experiments considers the cytochrome data sets from tdc. tdc provides five binary classification data sets for cytochrome inhibition based on the work of Veith et al. and three binary classification data sets for cytochrome substrates based on the work of Carbon-Mangels et al.