Welcome to PracticalCheminformatics! AI and machine-learning (ML) based tools are prevalent in academia and industry for a variety of applications, from pharmaceuticals and agrochemicals to solar cells and batteries. Cheminformatics has the potential to drastically improve the efficiency of chemists, however many scientists are not trained in the implementation of these tools (Python, GPUs, etc). To fill in this gap, I started PracticalCheminformatics, a practical guide to running free-to-use, open-source cheminformatics software. Using cloud computing resources via Google Colab notebooks, you will learn how to design new drug molecules, run molecular dynamics simulations and virtual screening with ML-guided docking, predict ADMET properties, use ML-guided retrosynthesis to guide your chemistry, use Bayesian Optimisation to guide your experiments, and much more! Whether you are new to the field or an experienced computational chemist, join the learning journey and enjoy the process!