New drug candidates often require bio-enabling formation technologies such as lipid-based formulations, solid dispersions, or nanosized drug formulations. Development of such more sophisticated delivery systems generally requires higher resource investment compared to a conventional oral dosage form, which might slow down clinical development. To achieve the biopharmaceutical objectives while enabling rapid cost effective development, it is imperative to identify a suitable formulation technique for a given drug candidate as early as possible. Hence many companies have developed internal decision trees based mostly on prior organizational experience, though they also contain some arbitrary elements. As part of the EU funded PEARRL project, a number of new decision trees are here proposed that reflect both the current scientific state of the art and a consensus among the industrial project partners. This commentary presents and discusses these, while also going beyond this classical expert approach with a pilot study using emerging machine learning, where the computer suggests formulation strategy based on the physicochemical and biopharmaceutical properties of a molecule. Current limitations are discussed and an outlook is provided for likely future developments in this emerging field of pharmaceutics. (c) 2021 American Pharmacists Association (R). Published by Elsevier Inc. All rights reserved.