In the last ten years scientists working on computational problems involving proteins and other small molecules have largely moved to using python when developing simulation and data analysis tools allowing for a fast prototyping and development of new ideas. One big challenge is dealing with the compatibility of different tools and using these to create very complex adaptive, yet robust workflows in order to be able to guide cutting edge experiments e.g. predicting how well a small drug like molecule can bind to a protein that could serve as a target for a new drug. The talk will give a gentle introduction to what kind of python related tools are available in the field of computational molecular biology, how they are used, and what kind of complex workflows scientist have to solve. I will then introduce BioSimSpace an open source python library and flagship project of the CCPBioSim consortium in the UK, which provides a common API to avoid having to learn many individual tools facing compatibility and dependency challenges allowing scientists to focus on the scientific question at hand and not solving programming challenges. BioSimSpace allows fast and interoperable building of workflow components (nodes) for bimolecular problems, which can easily be used on a variety of different computational resources. In particular I will introduce the cloud facilities available for fast prototyping using a Jupyter notebook interface.