A computer-implemented method of conducting directed evolution, the method comprising: (a) receiving an unfiltered data set having information from physical measurements of molecules, wherein the unfiltered data set comprises the following information for each of a plurality of variant biomolecules: (i) activity of the variant biomolecule on a ligand in a binding site of the variant biomolecule, (ii) a sequence of the variant biomolecule, wherein the sequence is a nucleic acid sequence or a protein sequence, and (iii) one or more geometric parameters characterizing the geometry of the ligand in the binding site of the variant biomolecule; (b) filtering the unfiltered data set to produce a filtered data subset, by removing information for one or more of the variant biomolecules, wherein the filtering removes at least one of the geometric parameters from the unfiltered data set and/or removes from the unfiltered data set certain variant biomolecules having geometric parameter values outside defined ranges, and wherein the filtering comprises training sequence activity models with a plurality of selected data subsets and testing the ability of the sequence activity models trained with the plurality of selected data subsets to predict the activity of a variant biomolecule on the ligand in the binding site of the variant biomolecule as a function of independent variables, and thereby identifying a filtered data subset that provides a sequence activity model with higher ability to predict the activity of a variant biomolecule on the ligand in the binding site of the variant biomolecule as a function of independent variables than a sequence activity model trained with the unfiltered data set, wherein the sequence of the variant biomolecule and filtered geometric parameters characterizing the geometry of the ligand in the binding site of the variant biomolecule are independent variables in the sequence activity models trained with the plurality of selected data subsets, each selected data subset having information for at least one of the geometric parameters and/or certain variant biomolecules having geometric parameter values outside defined ranges removed from the unfiltered data set of (a); and (c) applying a sequence activity model trained using the filtered data subset to identify one or more new biomolecule variants predicted to have activity meeting one or more criteria, wherein each of the one or more new biomolecule variants has a nucleic acid or protein sequence that differs from the sequences of the biomolecule variants providing information for the unfiltered data set of (a). (a) receiving an unfiltered data set having information from physical measurements of molecules, wherein the unfiltered data set comprises the following information for each of a plurality of variant biomolecules: (i) activity of the variant biomolecule on a ligand in a binding site of the variant biomolecule, (ii) a sequence of the variant biomolecule, wherein the sequence is a nucleic acid sequence or a protein sequence, and (iii) one or more geometric parameters characterizing the geometry of the ligand in the binding site of the variant biomolecule; (b) filtering the unfiltered data set to produce a filtered data subset, by removing information for one or more of the variant biomolecules, wherein the filtering removes at least one of the geometric parameters from the unfiltered data set and/or removes from the unfiltered data set certain variant biomolecules having geometric parameter values outside defined ranges, and wherein the filtering comprises training sequence activity models with a plurality of selected data subsets and testing the ability of the sequence activity models trained with the plurality of selected data subsets to predict the activity of a variant biomolecule on the ligand in the binding site of the variant biomolecule as a function of independent variables, and thereby identifying a filtered data subset that provides a sequence activity model with higher ability to predict the activity of a variant biomolecule on the ligand in the binding site of the variant biomolecule as a function of independent variables than a sequence activity model trained with the unfiltered data set, wherein the sequence of the variant biomolecule and filtered geometric parameters characterizing the geometry of the ligand in the binding site of the variant biomolecule are independent variables in the sequence activity models trained with the plurality of selected data subsets, each selected data subset having information for at least one of the geometric parameters and/or certain variant biomolecules having geometric parameter values outside defined ranges removed from the unfiltered data set of (a); and (c) applying a sequence activity model trained using the filtered data subset to identify one or more new biomolecule variants predicted to have activity meeting one or more criteria, wherein each of the one or more new biomolecule variants has a nucleic acid or protein sequence that differs from the sequences of the biomolecule variants providing information for the unfiltered data set of (a).