In this work, we present sparsity-enforcing linear models for molecular-property prediction. In our experiments, we seek to find quick baselines for single-task supervised-learning problems. Presented pipeline combines multiple types of calculated descriptors and fingerprints, applies basic feature normalization, optionally arranges combined features into groups, and selects a small group of informative features. Since the pipeline has no feature engineering and requires tuning mainly regularization hyperparameters, it can be a desirable baseline for high-dimensional, single-task molecular-property prediction.