The space of fingerprints is high dimensional; typically ~1,024, corresponding to the number of bits used for generating the fingerprint. After the clustering operation, AIMSim embeds the entire molecule set from the high dimensional space of fingerprints to two dimensions. This embedding is displayed as an interactive plot on the web browser. The user can visit various regions of the plot at varying scales and can hover around individual data points to get information about the molecule represented by the points. These interactive plots are generated using the Plotly Python package [28] and manipulated in the user's web browser. AIMSim currently implements multidimensional scaling (MDS) , Tdistributed Stochastic Neighbor Embedding (t-SNE) , Principal Component Analysis (PCA) , Isomap embedding and Spectral embedding for dimensionality reduction. AIMSim uses the scikit-learn implementations of these algorithms. Note that dimensionality reduction can only be done using similarity measures that yield a valid distance. This is enforced by AIMSim.