The classification of leukocyte sub-types is essential for medical diagnostics and treatments. Advances in this field have been driven by the creation of novel Deep Learning (DL) architectures, whose progress is sometimes marginal or not even comparable due to the use of proprietary data sets or different setups/partitions of public data sets. This study presents a novel synthetic image data set designed for both training and benchmarking, providing a standardised platform to evaluate advancements in this field. The data set includes two versions of differing complexity: straightforward and challenging. Experiments with various DL models showed unexpectedly higher accuracy, precision, and recall on the more complex data set. These results highlight the importance of data set complexity in assessing the robustness and effectiveness of DL models for complex medical image analysis tasks.