In terms of other relevant physicochemical variables, we observe moderate correlations (0.35 ≲ rPearson ≲ 0.65) between at least one PC and isoelectric point / charge at pH 7 in the AAE-64, RNN-32, RNN-64, Transformer-64 and all WAE models and moderate correlation between at least one PC and hydrophobicity in all but the RNN-Attention-32. As these are traditional hallmarks employed for AMP design, this is desirable behavior, and in particular aids in the interpretability of the models through a linear mapping of the latent space variables to straightforward "bridge variables" for most models. It also shows that the models are capable of identifying relevant properties from sequences alone, despite being trained only with a binary property predictor.