With the release of MolPipeline, we provide a tool covering a broad spectrum of use cases, such as, custom molecular standardization (e.g., salts removal), molecule-based and data-driven clustering (e.g. Murcko scaffolds), and molecular encoding. In addition, different encodings can be combined to a single feature vector, e.g. feature vectors of fingerprints and physicochemical properties. The final pipeline is compatible with scikit-learn's API and for hyperparameter optimization, model selection, and can be serialized for deployment or as a template for future projects. Compared to previous tools, a key feature in our work is the consistent handling of processing errors. As in real-world data sets, it is not uncommon to come across SMILES strings that cannot be successfully parsed. Automatic logging, filtering, or replacing these samples is mandatory to avoid manual intervention, which can become cumbersome for large data regimes and is infeasible for end-to-end productive deployment in automatic workflows.