After confirming that the DNN prediction model can accurately reproduce MD-level results (which we in turn showed to accurately reproduce experimental data), we now investigate its efficiency, in particular relative to MD. Our MD calculations for the subset of 100,000 molecules took a total of 5 million core hours of compute time on a modern high-performance computing cluster. For the entire screening library, this extrapolates to approximately 75 million core hours (In addition to the compute time, there is generally a considerable amount of human time required for the setup and execution of these calculations. In our study, many of these tasks were performed by ChemHTPS without manual intervention.) The demand on disk space is another issue, and we estimate a need for 120 terabytes for the entire library (15 terabytes without trajectories). The DNN prediction model produces essentially the same results in less than 10 core hours of compute time (without performance optimization), with all but 10 minutes of the time required to generate the feature matrix of the compound library. Disk use is marginal. This corresponds to a speed-up of about seven orders of magnitude, with negligible loss in accuracy. A speed-up of that magnitude allows a corresponding increase in the scale and scope that is affordable for screening studies. The bottleneck of our DNN prediction model is the generation of the training data needed for its creation. It is worth noting, though, that this is a fixed cost rather than an effort that scales with the number of compounds studied. The size of the employed training set (100,000 compounds corresponding to 5 million core hours) was originally chosen ad hoc. We now assess the learning curve as a function of training set size to gain insights into the actual data needs of our DNN model, which is one of the key questions in applying machine learning to any given problem setting. Our goal is to establish, how many data points are necessary to converge the learning process and/or achieve a desired accuracy. By minimizing the training set size requirement, we minimize the investment in computational resources needed to perform the expensive MD simulations. To address this question, we successively increase the size of the training set from 50 to 100,000 molecules. The resulting learning curve is shown in Fig. . We observe that all models trained on fewer than 2,000 data points (i.e., 2% of the available data) perform poorly. Models based on 2,000 to 4,000 data points offer acceptable accuracy. Those based on 4,000 to 6,000 data points offer very good accuracy, and at 10,000 data points, the training is essentially saturated and the learning curve plateaus off. Additional training data does not lead to an improvement of the DNN model and is essentially wasted. Thus, we do not require a large data set of 100,000 molecules to learn the packing density of organic molecules. We can develop an accurate model using MD data of just 5,000 molecules (or more conservatively 10,000). This reduces our demand of computing time from 5 million to less than 0.25 (or 0.5) million core hours (including additional data for the test set), which has significant implications for the cost-benefit analysis and viability of this approach.