Without experimental data, however, the predictive accuracy of any prospective ML model is fundamentally limited by the underlying performance of the DFT methodology, basis set, description of solvation, and other sources of computational error. Therefore, we pursued a transfer learning (TL) strategy, inspired by the work of Roitberg, Isayev, and co-workers 57 in which the accuracy of a NN potential extensively trained against DFT energetics could be enhanced using a much sparser dataset of high-quality CCSD(T) values. We demonstrate improvements in the predictive accuracy of a DFT-trained model by applying TL with a smaller collection of experimental values: following model retraining against a curated set of C experimental shifts, a mean absolute error (MAE) of 1.23 ppm against experiment could be obtained for 500 held-out structures (see below). This involved additional 5,000 experimental structures to the existing 8,000 DFT optimized structures. Taking a step further, we demonstrate that molecular geometries obtained from inexpensive molecular mechanics calculations can be used directly without a substantial loss in accuracy, generating chemical shift predictions on the order of 5-10,000 times faster than conventional electronic structure calculations.