We observe little or no (ρ AMP < 0.4 for all tested values) partitioning for the 32-and 64-dimensional AAE and RNN-Attention. We observe a semi-linear partitioning-as the PC is increased or decreased, an area of zero AMP probability is followed by a monotonic, almost linear rise in probability-for all other models except the 128 dimension RNN-Attention. Interestingly, the 64 dimensional RNN-Attention model shows a non-linear partitioning of its space with a region of high AMP probability, even though the PCA visualisation and the silhouette score for this model did not demonstrate a clustering of AMPs in its latent space. Thus, we show that predicting experimentally-verified antimicrobial properties, even though many of the non-hits may also be AMP-like in nature, is in many cases sufficient to partition the space into regions of high and low antimicrobial peptide probability in general.