Title: Key Design Choices for Double-transfer in Source-free Unsupervised Domain Adaptation. Abstract: Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not accessible in many real-world scenarios. This led to the development of Unsupervised Domain Adaptation (UDA) methods, which only employ unlabeled target samples. Furthermore, efficiency and privacy requirements may also prevent the use of source domain data during the adaptation stage. This particularly challenging setting, known as Source-free Unsupervised Domain Adaptation (SF-UDA), is still understudied. In this paper, we systematically analyze the impact of the main design choices in SF-UDA through a large-scale empirical study on 500 models and 74 domain pairs. We identify the normalization approach, pre-training strategy, and backbone architecture as the most critical factors. Based on our observations, we propose recipes to best tackle SF-UDA scenarios. Moreover, we show that SF-UDA performs competitively also beyond standard benchmarks and backbone architectures, performing on par with UDA at a fraction of the data and computational cost. Experimental data and code will be released upon acceptance.