Deep learning approaches allow for automatic discovery of valuable representations and simplify complex raw data to form "good" features by building and training deep neural networks. An autoencoder, a specific type of deep neural network, consists of encoder and decoder neural network architectures, where the encoder maps the input data into a compressed representation (also known as a latent representation) and the decoder maps the compressed representation back to the original input space. By training the autoencoder to minimise the reconstruction error between the input and output, the model learns a compressed representation that captures the salient features of the data. As a result, deep learning algorithms do not require the feature selection of traditional approaches and are able to automatically learn and extract useful features to detect anomalies. The Deep Support Vector Data Description (DeepSVDD) architecture, another neural network used in this paper, is sourced from Ruff et al. and adapted by Vriza et al. to accelerate the discovery of π-π co-crystals. In a similar principle with One-Class SVM (OCSVM), the DeepSVDD technique aims to identify a smallest hypersphere that contains all of the samples except for a limited number of outliers. This architecture is a two-step process. The first step, i.e., the pre-training step, is composed by an autoencoder for learning a compact data representation that captures the essential information about the data distribution and maps the input data into a hypersphere region. The second step, i.e., the training step, a Feed Forward Neural Network architecture is used to learn the transformation and optimise the centre of hypersphere with the aim of minimising the distance between all data representations and the centre.