Vetinformatics approaches, such as virtual screening, molecular dynamics (MD) simulations, and other molecular modeling techniques, are increasingly being used in veterinary drug discovery. These methods provide detailed information on the structural, conformational, dynamical, and thermodynamic properties of proteins and their complexes, which can be used to accelerate the drug discovery process, reduce costs, and improve the efficacy and safety of new drugs. Experimental methods such as X-ray crystallography provide a high-resolution static state of a structure, while MD simulation methods apply Newton’s equations of motion to observe the physical movement of atoms in a microscopic system (trajectory). High-level MD simulation programs such as GROMACS, AMBER, and CHARMM are used to sample the conformational space of drug targets. Interatomic forces and potential energy of a system are calculated by employing molecular mechanical force fields integrated with the program, while binding-free energy between a target protein and potential hit molecule is estimated through the Molecular Mechanics Poisson–Boltzmann surface area (MM-PBSA) method. These approaches are commonly used to prioritize veterinary drug candidates for future application in firms following successful clinical trials. For example, virtual screening can be used to identify potential drug candidates from a large library of compounds, while MD simulations can be used to study the drug-target interaction and predict the drug’s efficacy and toxicity. The integration of vetinformatics approaches into the drug discovery pipeline has the potential to revolutionize the development of new veterinary drugs. These methods can help to identify new drug targets, design more effective drugs, and predict their safety and efficacy more accurately.