  We use deep autoencoder neural networks to draw a chart of the heterotic
$\mathbb{Z}_6$-II orbifold landscape. Even though the autoencoder is trained
without knowing the phenomenological properties of the $\mathbb{Z}_6$-II
orbifold models, we are able to identify fertile islands in this chart where
phenomenologically promising models cluster. Then, we apply a decision tree to
our chart in order to extract the defining properties of the fertile islands.
Based on this information we propose a new search strategy for
phenomenologically promising string models.
