The accurate prediction of the bound-like conformations of small molecules is crucial for many computational methods in drug development, including virtual screening , structure-based drug design , molecular docking , and pharmacophore modeling . Exploring the vast conformational space of flexible molecules efficiently is still challenging, especially when the bioactive conformation is unknown or differs significantly from low-energy states. While numerous conformer generation tools exist, there is a pressing need for methods that can balance accuracy, precision, diversity, geometrical correctness, and computational efficiency. The landscape of conformer generation is diverse, encompassing traditional software tools and, more explore the torsional space of molecules, proving effective for compounds with a moderate number of rotatable bonds. However, stochastic sampling techniques often prove more suitable for highly flexible molecules, such as macrocycles , employing random sampling to handle a broader range of molecular flexibilities and ring template libraries . The distance geometry (DG) approach implemented in RDKit defines upper and lower distance bounds between all pairs of atoms in a molecule . These bounds are typically derived from chemical knowledge, experimental data, and structural constraints. DG approach generates conformations by randomly selecting distances within these bounds for each atom pair, followed by an embedding process that mathematically transforms the interatomic distance matrices into three-dimensional Cartesian coordinates, thereby generating the final molecular conformations. Knowledge-based approaches have emerged as a fast and often accurate alternative to sample every rotatable bond, incorporating libraries of pre-computed torsion angles and fragment conformations derived from experimental data . For applications demanding high accuracy, force field-based methods offer a physicsbased approach using molecular mechanics, while quantum chemical approaches like the Conformer-Rotamer Ensemble Sampling Tool (CREST) method employ semiempirical calculations to guide conformer generation. CREST combines metadynamics simulations with semiempirical calculations employing a history-dependent biasing potential based on atomic RMSD as a collective variable to explore the potential energy surface. Through iterative sampling and geometry optimization, CREST generates conformational ensembles that account for electronic effects. This approach has proven effective for complex molecular systems such as macrocycles and metal-containing compounds , where conventional force field methods show limitations. However, despite using relatively efficient semiempirical methods, CREST calculations remain computationally demanding and require significantly longer processing times compared to traditional approaches. In this work, we introduce Moltiverse, a novel approach for conformer generation using enhanced sampling molecular dynamics (MD) simulations to explore the conformational landscape of small molecules. At its core, Moltiverse combines the extended adaptive biasing force algorithm with metadynamics (M-eABF), guided by a single collective variable -the radius of gyration (RDGYR). This unique combination enables efficient and exhaustive generation of conformer ensembles in a short simulation time, overcoming energy barriers that often trap traditional MD simulations in local minimum. Unlike systematic and stochastic approaches, Moltiverse's MD-based method uses a physics-based potential for the exploration of conformational space, which avoids generating potentially fictitious geometries. This approach is particularly advantageous for molecules with complex energy landscapes or those with a high number of rotatable bonds, where traditional methods might struggle to sample relevant conformations efficiently.