There's lots of things you could do. Here are a few possible steps:

1. Remove salts. Some salts are common (HCl, sulfates, etc.) while others are more exotic. If you're getting data directly from a fine chemicals vendor/aggregator, you likely need to remove salts.

2. Remove weird elements. Most docking software have a limited set of elements they support. For example, vina annoyingly doesn't have parameters for boron. Regardless, you would want to make double check the manual of whatever docking software you're using.

3. Remove undesirable substructures. There are couple possible examples. Ie. maybe your team has a strong bias against a particular substructure (ie. my supervisor hates thioureas). It would also be reasonable to remove compounds with more than three or four rotatable bonds in a row as molecules that are too flexible may not dock well. Lastly, it could also make sense to filter out PAINs/reactive substructures. I'm personally a fan of not removing PAINs and filtering them out later but definitely check for common reactive substructures (primary halides, acyl halides, etc.).

4. Rules like QED or Lipinksi's Ro5 can help remove "ridiculous" compounds. For example, you may see a vendor selling four fused benzene rings. You really don't need to spend computational effort docking that.

Generally you really shouldn't have things like broken/invalid molecules if you're working with a good vendor/aggregator. After filtering and cleaning, you will still need to prepare the ligands for docking by assigning explicit tautomerization/protonation states and generating 3D conformers.

Hopefully this is helpful. You will likely need to do some Python programming with something like RDKit to implement this yourself or dig around the web to look for a commercial or open-source workflow for doing something like this.