In this three-study investigation, we applied various approaches to score drawings created in response to both Form A and Form B of the Torrance Tests of Creative Thinking-Figural (broadly TTCT-F) as well as the Multi-Trial Creative Ideation task (MTCI). We focused on TTCT-F in Study 1, and utilizing a random forest classifier, we achieved 79% and 81% accuracy for drawings only (r = 0.57; 0.54), 80% and 85% for drawings and titles (r = 0.59; 0.65), and 78% and 85% for titles alone (r = 0.54; 0.65), across Form A and Form B, respectively. We trained a combined model for both TTCT-F forms concurrently with fine-tuned vision transformer models (i.e., BEiT) observing accuracy on images of 83% (r = 0.64). Study 2 extended these analyses to 11,075 drawings produced for MTCI. With the feature-based regressors, we found a Pearson correlation with human labels (rs = 0.80, 78, and 0.76 for AdaBoost, and XGBoost, respectively). Finally, the vision transformer method demonstrated a correlation of r = 0.85. In Study 3, we re-analyzed the TTCT-F and MTCI data with unsupervised learning methods, which worked better for MTCI than TTCT-F but still underperformed compared to supervised learning methods. Findings are discussed in terms of research and practical implications featuring Ocsai-D, a new in-browser scoring interface.