Comparing a target molecule to a database is vital for locating molecules with similar or different properties and narrowing the candidates to explore experimentally or computationally. For example, one may need to replace a top-performing but toxic solvent with a green one of similar properties. Conversely, it might be required to select the set of molecules most different from a query molecule. Such a use case is typical in designing a training set for a machine learning model, where one is interested in identifying molecules that enhance the diversity of the training set or making the model more robust and generalizable to unseen data. The task generates a pairwise similarity distribution quantifying the similarity between a target molecule and an entire molecule set, its most similar and dissimilar molecules, and the top "n" most similar and dissimilar molecules. Optionally, AIMSim also generates a structural representation of these molecules. An example is shown in Case Study #1.