In silico models for predicting the physicochemical, biological, and structural properties of compounds can substantially enhance efficiency in drug discovery and related fields. For example, these models offer guidance on optimizing experimental resources and prioritizing compound synthesis. For building in silico models, data representation in a machinereadable format is a prerequisite. Molecules, for example, can be represented by their physicochemical properties, 3 topological fingerprints, 4,5 or molecular graphs. Historically, simple linear models were used for assessing and predicting molecular properties. Although globally interpretable, their applicability is limited due to low expressive power. Today, machine-learning methods like random forests (RFs), support vector machines (SVMs), and neural networks (NNs) prevail in molecular property prediction. While all are non-linear, their properties and behavior can vary, especially concerning data rotation or scale. Deep learning has garnered significant interest due to its ability to learn internal representations (embeddings) of data points directly from the data rather than relying on expert selection. This approach allows for the introduction of inductive bias, tailoring the NN architecture to the specific problem at hand. This flexibility is particularly beneficial for handling high-dimensional data in data-limited regimes and it allows NNs to process nonvector representations of data. For instance, convolutional neural networks (CNNs) reduce the number of connections between layers by employing learnable weights within sliding filters applied to images. Similarly, graph neural networks (GNNs) process graphs in a permutation-invariant manner. Additionally, there has been a surge in the popularity of models capable of handling sequence data, fueled by recent advancements in natural language processing. Model analysis is essential to foster the creation of effective models but can be challenging, particularly for non-linear models. When a model is expected to exhibit certain desirable properties, employing a model analysis tool can effectively demonstrate whether the model indeed possesses these attributes. For example, similarity measures for embeddings can be used to observe changes in embedding distributions. Therefore, tools like similarity measures for embeddings are crucial in navigating the complexities of model analysis.