The computational teams chose approaches from a vast variety of different methods (see Figure ) and therefore considered diverse orthogonal approaches, yet there is a field of machine learning methods which seems underrepresented from a today's perspective. In the field of machine learning methods for drug discovery, recently, a lot of work was done for few-and zero-shot learning methods. Few-(and zero-) shot learning methods are machine learning methods which need only little (or none) training data for a new drug target (i.e., machine learning task). Usually, these methods are pre-trained on a large amount of data, and then adapted and used for targets for which just a few measurements are available. This is why few-shot learning methods are an intuitive fit to the COVID-19 setting and, indeed, some of the computational teams used similarity-based approaches which can be seen as few-shot methods, e.g. classic similarity search and k-nearest neighbors . Since most of the few-shot methods specialized for drug discovery have arisen after the start of this project, we believe that these methods would have been used more, if we started the project today.