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The advent of Cloud computing has paved the way to envision hybrid computational infrastructures based on powerful Grid resources combined with dynamic and elastic on-demand virtual infrastructures on top of Cloud deployments. However, the combination of Grid and Cloud resources for executing computationally intensive scientific applications introduces new challenges and opportunities in areas such as resource provisioning and management, meta-scheduling and elasticity. This paper describes different approaches to integrate the usage of Grid and Cloud-based resources for the execution of High Throughput Computing scientific applications. A reference architecture is proposed and the the opportunities and challenges of such hybrid computational scenarios are addressed. Finally, a prototype implementation is described and a case study that involves a protein design application is employed to outsource job executions to the Cloud when Grid resources become exhausted.