Abstract: Deep generative diffusion models are a promising avenue for 3D $\textit{de novo}$ molecular design in material science and drug discovery. However, their utility is still constrained by suboptimal performance with large molecular structures and limited training data. Addressing this gap, we explore the design space of E(3) equivariant diffusion models, focusing on previously blank spots. Our extensive comparative analysis evaluates the interplay between continuous and discrete state spaces. Out of this investigation, we introduce the EQGAT-diff model, which consistently surpasses the performance of established models on the QM9 and GEOM-Drugs datasets by a large margin.Distinctively, EQGAT-diff takes continuous atomic positions while chemical elements and bond types are categorical and employ a time-dependent loss weighting that significantly increases training convergence and the quality of generated samples.To further strengthen the applicability of diffusion models to limited training data, we examine the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogens to target distributions with explicit hydrogens. Fine-tuning EQGAT-diff for a couple of iterations further pushes state-of-the-art performance across datasets.We envision that our findings will find applications in structure-based drug design, where the accuracy of generative models for small datasets of complex molecules is critical.