To exhaustively screen the entire chemical space a fast estimation of the storage density and back-reaction barrier is needed. We represent each molecule as a 287 (7 x 41) bit vector, where each row represents one of the seven open positions on the DHA motif, and each column (41) represents a non-hydrogen substituent. Using this representation, we fit two linear regression models to SQM storage densities. One model predicts storage densities for molecules with ≤4 substituents and the other model for molecules with more than 4 substituents. We found that two different models are needed to get an acceptably low error (MAEs of 0.02 and 0.04 kJ/g, respectively). Each model is trained on ∼25,000 molecules and tested on another ∼25,000 molecules that represent a wide range of storage densities (Figure ). Figure shows the number of times a ligand is found at a given The simplicity of the linear ML-models means that they can be used to perform an exhaustive search of all 230 billion molecules in only 12 hours using a single CPU. During the exhaustive search, all molecules with predicted storage densities smaller than 0.30 kJ/g are discarded (Figure ). With the linear regression model, we reduce the chemical space of interest from 230 billion to roughly 421 million molecules. As seen from Table , molecules with 5-7 substituents greatly outnumber molecules with 1-4 substituents.