We have presented the battery screening capabilities of VOLTA, a Python software developed in the present work. VOLTA allows for a simple, back-of-the-envelope style of screening that can applied to any ensemble of electrode entities. The core concept is to screen battery materials indirectly, allowing the researcher to focus on the desired cell properties. The software also helps the user in assessing the feasibility of the constructed cells by producing ad-hoc metrics and approximates the real-world performance of the composite electrodes by relying on the physics-based manufacturing model of the ARTISTIC project. VOLTA relies heavily on the data it is input with. A battery revealing task can be conducted with DFT-based data, like in the present work. This approach has several advantages: the data is potentially uniform, as the energetic properties can all be calculated with the same density functional, and the data collection is relatively inexpensive, especially when collecting DFT data from online databases like the MP. This in turn can result in a "cutting-edge" battery revealing task, limited only by the chemical diversity of the AM dataset.