Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, and promoting diversity and novelty. Recent advances in computer vision introduce a range of new guidance strategies that can be adapted for these goals. In this work, we integrate state-of-the-art guidance methods — including classifier-free guidance, autoguidance, and model guidance — into a leading molecule generation framework built on an SE(3)-equivariant flow matching process. We propose a hybrid guidance strategy that separately guides continuous and discrete molecular modalities — operating on velocity fields and predicted probabilities, respectively — while jointly optimizing their guidance scales via Bayesian optimization. Our implementation benchmarked on the QM9 dataset achieves new state-of-the-art performance in property alignment for de novo molecular generation. The generated molecules also exhibit high structural validity. Furthermore, we systematically compare the strengths and limitations of various guidance methods, offering insights into their broader applicability.