The increasing amount of data and the focus on data-driven methods call for reproducible and reliable pipelines for computer-aided drug design (CADD). Knowing how to access and use these resources programmatically, however, requires domain-specific training and inspiration. The TeachOpenCADD platform showcases webserver-based data acquisition and common tasks in the fields of cheminformatics and structural bioinformatics. The theoretical and programmatic aspects of each topic are outlined side-by-side in Jupyter notebooks (talktorials) using open source resources only. To foster FAIR research, we apply software best practices such as testing, continuous integration, and idiomatic coding throughout the whole project. The talktorials are accessible via our website, Binder, and conda package to accommodate different use cases such as reading, executing, and modifying, respectively. We believe that TeachOpenCADD is not only a rich resource for CADD pipelines and teaching material on computational concepts and programming but as well a good example of how to set up websites, automated testing, and packaging for notebook-centric repositories. TeachOpenCADD is a living resource; problems can be voiced via GitHub issues and contributions can be made in the form of pull requests on GitHub. TeachOpenCADD is meant to grow; everyone is welcome to add new topics. Whenever you explore a new topic for your work, we invite you to fill our talktorial template with what one learns along the way and to submit it to TeachOpenCADD.