Abstract: QSAR techniques have proven to be extremely valuable in pharmaceutical research, particularly 3D QSAR. However, the necessity of descriptor calculation and selection or conformer generation, and structural alignment makes use of QSAR non-trivial. Demands for analysis of large data sets such as those being generated by combinatorial chemistry and high throughput screening have compounded this problem. Hologram QSAR (HQSAR) is a new technique which employs specialised fragment fingerprints (molecular holograms) as predictive variables of biological activity or other structural related data. By removing the necessity for molecular alignment, models by HQSAR can be obtained more rapidly than other by techniques, which makes HQSAR readily applicable to both small and large data sets. HQSAR models are comparable in predictive ability to those derived from CoMFA studies and allow extension to database searching capability.