Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. In this talk we will discuss FAIR principles for computational scientific workflows. We will highlight remaining challenges to address the workflows specific nature in terms of their composition of executable software steps, their provenance, and their development.