Transfer-learning methods aim to improve performance in a data-scarce target domain using a model pretrained on a source domain. A cost-efficient strategy, , involves freezing the source model and training a new classification head for the target domain. This strategy is outperformed by a more costly but state-of-the-art method--- all parameters of the source model to the target domain---possibly because fine tuning allows the model to leverage useful information from intermediate layers which is otherwise discarded. We explore the hypothesis that these intermediate layers might be directly exploited by linear probing. We propose a method, , that selects features from all layers of the source model to train a target-domain classification head. In evaluations on the Visual Task Adaptation Benchmark, Head2Toe matches performance obtained with fine tuning on average, but critically, for out-of-distribution transfer, Head2Toe outperforms fine tuning.