Several bioinformatic techniques can support the prioritization and selection of hits for invitro conrmation of antigen binding as well as lead identication. A commonly used strategy is to cluster the hits into bins of high, medium, and low binding afnity, based on the initial estimates, analyze each bin for the diversity of heavy and light chain germlines followed by diversity of the CDRs, and select mul­tiple but few representatives from each germline pair in each bin for experimental testing. Alternatively, one could directly bin the hits based on the germline pairings and CDR diversity and select multiple but few of them based on their estimated antigen binding. In addition to the antigen binding, the aspect of developability can already be considered early at this point of hit selection by employing computa­tional tools. In a simple application, one can score heavy (HC) and light chain (LC) sequences of hits based on presence of potential chemical degradation motifs, aggregation prone regions (APRs), and T-cell immune epitopes present or overlap­ping with the CDRs of the heavy and light chains. Such scoring schemes can be further optimized by adding different weights based on which CDRs contain these motifs and whether they are present in the at the beginning/end or in the middle of the CDRs. In a more structure-based approach, three-dimensional homology mod­els of all or a subset of hits can be analyzed regarding physicochemical descriptors such as pI, charge, dipole moment, and solvent exposed hydrophobic and ionic patches [22, 24]. In subsequent studies, one or few of the best hits are experimen­tally tested thoroughly for biological function, cross-reactivity across species, non­specic binding, and pharmacology indicators such as serum stability. This process culminates into identication of one or more lead candidates.