Carbon capture and storage is part of the roadmap towards net zero for many countries around the world, since emissions from existing infrastructure are close to estimated carbon budgets. To address this problem, currently 87 carbon capture projects are proposed worldwide in the next 10 years. A major class of commercial carbon capture technology involves capture systems using solvents. Commonly carbon capture solvents feature blends of amines and water. Whilst these blends have proved valuable there is an increasing need to identify new candidate molecules which are more efficient and improve performance. Systematic approaches to improve on the current technology are now needed with increasing urgency to expedite the introduction of cutting edge carbon capture methods. Here, we present a chemical space analysis of amines and carbon capture usage. We proceed to show a framework for computational screening relevant to carbon capture solvents. We demonstrate the use of cloud computing, novel molecular representations and machine learning to screen potential candidates. We show the utility of machine learning in this field for high throughput virtual screening with an exemplar application to absorption capacity classification. Additionally, we highlight the need for improved data awareness and accessibility to enable this field to advance at a pace commensurate to its global importance. Our research brings together multiple methods and domains of expertise to accelerate the discovery of carbon capture solvents.