We present RanDepict, a toolkit for generating diverse representations of chemical structures. It addresses the problem of the generation of diverse training data for OCSR tools by pseudorandomly setting the available depiction parameters when depicting a structure with one out of three cheminformatics toolkits (Chemistry Development Kit (CDK) , RDKit and Indigo ). Various augmentations such as image perturbations or non-structural elements like labels and curved arrows can also be added. Instead of pseudo-randomly picking depiction and augmentation parameters, there also is the option to generate the images based on depiction feature fingerprints. Here, the depiction and augmentation parameters are represented as bit arrays and RDKit's implementation of the MaxMin algorithm is used to pick diverse samples out of all valid fingerprints. By making it publicly accessible, we hope to contribute to the development of robust deep learning-based OCSR systems by providing diverse training and benchmark datasets. RanDepict's source code is publicly available on GitHub.