Researchers prove novel results related to transfer learning, highlighting the need for careful selection of transferred information and dependence on target problems, and establish an upper bound on the amount of improvement possible through transfer learning. This work builds on the algorithmic search framework for machine learning, making the results applicable to a wide range of learning problems using transfer.