The computational teams used a variety of machine learning , docking and hybrid approaches (Figure ). In the group of machine learning based methods, approaches included: reinforcement learning, random forests , gradient boosting , kernel-based methods-e.g., Vanishing Ranking Kernels -and deep learning methods-e.g., self-normalizing-networks , LSTMs , CNNs , geometric deep learning, and graph neural networks . Also stochastic-based methods-e.g., Naive Bayes Classifier 51 and Self-Consistent Regression 52were used. The docking teams used different tools like GLIDE , AutoDock Vina , QVINA2, VirtualFlow , Fred, Smina, Gold , PLANTS and Data Warrior. Some teams considered molecular dynamics simulations. Others combined machine learning with conventional docking approaches. This was done by a) building a pipeline in which different computational methods were stacked on top of each other-e.g., some groups used machine learning methods to make a pre-selection of the screened compounds and then used docking methods for the most promising compounds-or b) using machine learning models as a scoring function for the docking methods. Also similarity-based methods-e.g., classic similarity search , feature tree search , and the knn-algorithm -and methods for dimensionality reduction-e.g., PCA, t-SNE, and GTM 65,66 -were used. Some teams added ADMET and PAINS filters to their virtual screening pipeline. On the ligand-side, multiple different molecular representations were used, e.g., SMILES, substructure-based descriptors (ECFPs), MACCs keys, continuous and data-driven molecular descriptors (CDDD) , MNA and QNA 52 descriptors. In terms of the hit rate, the team that ended up with the most compounds (team jku, see below) used descriptor-based deep learning methods with small molecules as inputs, thus a ligand-based approach. The self-normalizing network approach renders the models robust against domain shifts from training data to testing data. The second-ranked method by team kyuken used shallow, ligand-based, and descriptor-based machine learning methods as a first step and subsequently used structure-based approaches to refine the search. The hit compound of kyuken showed significant viral reduction in cell-based assays (see section 2.6.5 below). The third-ranked method (team aiwinter) used docking-based methods and QSAR models. For details, see Supporting Information Section 1.