Deep metric learning (DML) methods generally do not incorporate unlabelled data. We proposeperforming borrowing components of the variational autoencoder (VAE) methodology to extend DML methods to train on semi-supervised datasets. We experimentally evaluate atomic benefits to the performing DML on the VAE latent space such as the enhanced ability to train using unlabelled data and to induce bias given prior knowledge