The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme, called alchemical perturbation density functional theory (APDFT), for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on the Kohn-Sham density functional theory (DFT) reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present benchmarking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH 4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are performed, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations. K E Y W O R D S adsorption energies, barrier heights, binding energies, computational catalysis, density functional theory, nudged elastic band calculations 1 | INTRODUCTION Advances in computational chemistry open new possibilities for impressively large-scale computational screening of hypothetical catalysts across materials space. [1-3] However, productively leveraging high-throughput screening has been challenging. For useful and insightful predictions, computational screening studies must be reproducible while also (a) determining important active sites that are stable under specified environmental conditions on large numbers of material compositions and (b) elucidating important elementary reaction steps with barrier heights that are