We demonstrate a trade-off between model complexity and model interpretability under this paradigm and suggest that for optimized design of AMPs in a continuous latent space, it may be appropriate to perform the optimization in multiple different latent spaces, using a similar philosophy to that of ensemble voting. We do a short case study to show one way this might be implemented, and indeed, in future work we plan to use this as a starting point for an active learning procedure to traverse these spaces and perform multi-scale molecular dynamics simulations upon relevant points.