Visualization-based cheminformatics tools are crucial in complex scenarios where data scientists and chemists analyze large sets of compounds and the output of AI models and XAI methods. Many goals in this context (e.g., to improve model accuracy) can be achieved by executing a series of abstract analytical tasks that will lead to data-driven decision-making. Each task can be carried out with the support of a variety of technologies, such as specific human-interaction and visualization techniques. Based on the experience acquired in our collaborations with data scientists and chemists, we identified three main tasks (T) that help them to achieve their goals. For each task, we explain why it is relevant, give a few examples of how it can be performed, and relate it to use cases defined in this article in which it is a key element of the analysis: