Our structure generation pipeline consists of two main steps. Initially we train multiple instances of LoGAN, a modified Wasserstein-GAN (WGAN) model, which are subsequently used to generate candidate structures. Given the finite number of physically plausible structures, this approach inevitably results in multiple candidates residing within the same energy basins. To avoid unnecessary computations we subsequently apply a postgeneration filter step to select representatives of these basins to be evaluated, which is based on structural descriptors and thus does not require an energy evaluation. The training data consists solely of a collection of structures, and also does not require their energies to be known. We note, however, that the trained model will sample from a distribution similar to the training set. Therefore, to search for low energy structures, the training set should primarily consist of low energy structures. In the following the various parts of our pipeline are discussed in detail.